├── .github ├── actions │ └── setup-torchts │ │ └── action.yml ├── dependabot.yml ├── labeler.yml └── workflows │ ├── docs.yml │ ├── labeler.yml │ ├── release.yml │ ├── security.yml │ └── test.yml ├── .gitignore ├── .pre-commit-config.yaml ├── CHANGELOG.md ├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── codecov.yml ├── docs ├── Makefile ├── make.bat └── source │ ├── _static │ ├── css │ │ └── custom.css │ └── images │ │ ├── favicon.ico │ │ └── torchTS_logo.png │ ├── _templates │ └── theme_variables.jinja │ ├── conf.py │ ├── index.rst │ ├── modules.rst │ └── torchts.rst ├── examples ├── dcrnn │ ├── README.md │ ├── main.py │ └── requirements.txt ├── mis-regression │ ├── README.md │ ├── lstm-mis-regression.ipynb │ └── requirements.txt ├── ode │ ├── SEIR │ │ ├── SEIR model with ODESolver.ipynb │ │ ├── SIR_SEIR_Test.ipynb │ │ └── SIR_data_SD_county.pt │ └── lorenz63 │ │ ├── Lorenz63Test.ipynb │ │ └── Lorenz63Test_UnobservedMethod.ipynb ├── quantile-regression │ ├── README.md │ ├── lstm-quantile-regression.ipynb │ └── requirements.txt └── seq2seq │ └── End2end pipeline with traffic dataset (GPU Tesla T4, Train 25mins).ipynb ├── poetry.lock ├── pyproject.toml ├── scripts └── build_docs.sh ├── tests ├── nn │ ├── test_loss.py │ └── test_ode.py ├── test_model.py └── test_sliding_window.py ├── torchts ├── __init__.py ├── nn │ ├── __init__.py │ ├── graph.py │ ├── loss.py │ ├── model.py │ └── models │ │ ├── __init__.py │ │ ├── dcrnn.py │ │ ├── ode.py │ │ └── seq2seq.py └── utils │ ├── __init__.py │ ├── data.py │ └── graph.py └── website ├── .gitignore ├── README.md ├── babel.config.js ├── docs ├── deep-sequence-to-sequence-models │ ├── _category_.json │ ├── seq2seq-gru.md │ └── seq2seq-lstm.md ├── deep-spatiotemporal-models │ ├── _category_.json │ ├── convolutional-lstm.md │ └── diffusion-convolutional-lstm.md ├── intro.md ├── linear-time-series-models │ ├── _category_.json │ ├── autoregressive.md │ └── vector-autoregressive.md ├── physics-guided-deep-sequence-models │ ├── _category_.json │ ├── autoode.md │ └── hybrid-autoode.md └── uncertainty-quantification-methods │ ├── _category_.json │ ├── mean-interval-score-regression.md │ ├── quantile-regression.md │ └── stochastic-gradient-mcmc.md ├── docusaurus.config.js ├── package-lock.json ├── package.json ├── sidebars.js ├── src ├── components │ ├── Container │ │ └── index.jsx │ ├── GridBlock │ │ └── index.jsx │ ├── HomepageFeatures.js │ ├── HomepageFeatures.module.css │ └── MarkdownBlock │ │ └── index.jsx ├── css │ └── custom.css └── pages │ ├── index.js │ └── index.module.css └── static ├── .nojekyll └── img ├── logo.png ├── logo2.png ├── puzzle.png ├── pytorch-logo.png ├── scalable.png ├── time-series-graph.png ├── torchTS_logo.png └── why.png /.github/actions/setup-torchts/action.yml: -------------------------------------------------------------------------------- 1 | name: Set up TorchTS 2 | description: Set up a development environment for TorchTS 3 | 4 | inputs: 5 | python-version: 6 | description: Python version to use with actions/setup-python 7 | default: 3.x 8 | 9 | outputs: 10 | python-version: 11 | description: Selected Python version information 12 | value: ${{ steps.python-version.outputs.version }} 13 | 14 | runs: 15 | using: composite 16 | steps: 17 | - name: Set up Python ${{ inputs.python-version }} 18 | uses: actions/setup-python@v5 19 | with: 20 | python-version: ${{ inputs.python-version }} 21 | 22 | - name: Get Python version 23 | id: python-version 24 | run: echo version=$(python -c "import sys; print('-'.join(str(v) for v in sys.version_info))") >> $GITHUB_OUTPUT 25 | shell: bash 26 | 27 | - name: Install Poetry 28 | uses: snok/install-poetry@v1 29 | with: 30 | virtualenvs-create: true 31 | virtualenvs-in-project: true 32 | 33 | - name: Restore Poetry cache 34 | uses: actions/cache@v4 35 | with: 36 | path: .venv 37 | key: poetry-${{ runner.os }}-${{ steps.python-version.outputs.version }}-${{ hashFiles('poetry.lock') }} 38 | 39 | - name: Install dependencies 40 | run: poetry install 41 | shell: bash 42 | -------------------------------------------------------------------------------- /.github/dependabot.yml: -------------------------------------------------------------------------------- 1 | version: 2 2 | updates: 3 | - package-ecosystem: pip 4 | directory: / 5 | schedule: 6 | interval: monthly 7 | labels: 8 | - dependencies 9 | - python 10 | 11 | - package-ecosystem: github-actions 12 | directory: / 13 | schedule: 14 | interval: monthly 15 | labels: 16 | - dependencies 17 | - github actions 18 | -------------------------------------------------------------------------------- /.github/labeler.yml: -------------------------------------------------------------------------------- 1 | dependencies: 2 | - changed-files: 3 | - any-glob-to-any-file: 4 | - poetry.lock 5 | - website/package-lock.json 6 | 7 | documentation: 8 | - changed-files: 9 | - any-glob-to-any-file: 10 | - docs/** 11 | - website/** 12 | - scripts/build_docs.sh 13 | - README.md 14 | 15 | examples: 16 | - changed-files: 17 | - any-glob-to-any-file: examples/** 18 | 19 | github actions: 20 | - changed-files: 21 | - any-glob-to-any-file: .github/** 22 | 23 | pre-commit: 24 | - changed-files: 25 | - any-glob-to-any-file: .pre-commit-config.yaml 26 | 27 | python: 28 | - changed-files: 29 | - any-glob-to-any-file: 30 | - '*.py' 31 | - poetry.lock 32 | 33 | source: 34 | - changed-files: 35 | - any-glob-to-any-file: torchts/** 36 | 37 | test: 38 | - changed-files: 39 | - any-glob-to-any-file: tests/** 40 | -------------------------------------------------------------------------------- /.github/workflows/docs.yml: -------------------------------------------------------------------------------- 1 | name: Docs 2 | 3 | on: 4 | push: 5 | branches: 6 | - main 7 | paths: 8 | - 'docs/**' 9 | - 'scripts/**' 10 | - 'torchts/**' 11 | - 'website/**' 12 | - 'poetry.lock' 13 | pull_request: 14 | branches: 15 | - main 16 | paths: 17 | - 'docs/**' 18 | - 'scripts/**' 19 | - 'torchts/**' 20 | - 'website/**' 21 | - 'poetry.lock' 22 | 23 | jobs: 24 | build: 25 | name: Build 26 | runs-on: ubuntu-latest 27 | 28 | steps: 29 | - name: Checkout 30 | uses: actions/checkout@v4 31 | 32 | - name: Set up TorchTS 33 | uses: ./.github/actions/setup-torchts 34 | with: 35 | python-version: 3.9 36 | 37 | - name: Set up Node 38 | uses: actions/setup-node@v4 39 | with: 40 | node-version: 14 41 | 42 | - name: Build Sphinx documentation 43 | run: scripts/build_docs.sh 44 | 45 | - name: Build Docusaurus website 46 | run: | 47 | cd website 48 | npm install 49 | npm run build 50 | 51 | - name: Generate token 52 | if: success() && github.event_name == 'push' 53 | id: generate-token 54 | uses: tibdex/github-app-token@v2 55 | with: 56 | app_id: ${{ secrets.APP_ID }} 57 | private_key: ${{ secrets.APP_PRIVATE_KEY }} 58 | 59 | - name: Deploy documentation 60 | if: success() && github.event_name == 'push' 61 | uses: JamesIves/github-pages-deploy-action@v4 62 | with: 63 | token: ${{ steps.generate-token.outputs.token }} 64 | git-config-name: torchts-bot[bot] 65 | git-config-email: 88511308+torchts-bot[bot]@users.noreply.github.com 66 | branch: gh-pages 67 | folder: website/build 68 | clean: true 69 | clean-exclude: | 70 | README.md 71 | -------------------------------------------------------------------------------- /.github/workflows/labeler.yml: -------------------------------------------------------------------------------- 1 | name: Label pull request 2 | 3 | on: 4 | - pull_request_target 5 | 6 | jobs: 7 | label: 8 | name: Label pull request 9 | runs-on: ubuntu-latest 10 | 11 | steps: 12 | - name: Checkout 13 | uses: actions/checkout@v4 14 | 15 | - name: Generate token 16 | id: generate-token 17 | uses: tibdex/github-app-token@v2 18 | with: 19 | app_id: ${{ secrets.APP_ID }} 20 | private_key: ${{ secrets.APP_PRIVATE_KEY }} 21 | 22 | - name: Label pull request 23 | uses: actions/labeler@v5 24 | with: 25 | repo-token: ${{ steps.generate-token.outputs.token }} 26 | sync-labels: true 27 | -------------------------------------------------------------------------------- /.github/workflows/release.yml: -------------------------------------------------------------------------------- 1 | name: Release 2 | 3 | on: 4 | push: 5 | tags: 6 | - v*.*.* 7 | workflow_dispatch: 8 | 9 | jobs: 10 | build: 11 | name: Build 12 | runs-on: ubuntu-latest 13 | 14 | steps: 15 | - name: Checkout 16 | uses: actions/checkout@v4 17 | 18 | - name: Set up Python 19 | uses: actions/setup-python@v5 20 | with: 21 | python-version: 3.9 22 | 23 | - name: Install Poetry 24 | uses: snok/install-poetry@v1 25 | with: 26 | virtualenvs-create: true 27 | virtualenvs-in-project: true 28 | 29 | - name: Install dependencies and build project 30 | run: | 31 | poetry install --without test,docs 32 | poetry build 33 | 34 | - name: Upload build artifacts 35 | uses: actions/upload-artifact@v4 36 | with: 37 | name: poetry-build 38 | path: dist/ 39 | 40 | github: 41 | name: GitHub 42 | runs-on: ubuntu-latest 43 | needs: build 44 | 45 | steps: 46 | - name: Checkout 47 | uses: actions/checkout@v4 48 | 49 | - name: Download build artifacts 50 | uses: actions/download-artifact@v4 51 | with: 52 | name: poetry-build 53 | path: dist 54 | 55 | - name: Get changes 56 | if: github.event_name == 'push' 57 | run: | 58 | tag=${GITHUB_REF#refs/tags/v} 59 | pattern="0,/$tag/d;/[0-9]\+\.[0-9]\+\.[0-9]\+/Q" 60 | sed $pattern CHANGELOG.md | head -n -1 | tail -n +2 > RELEASE.md 61 | cat RELEASE.md 62 | 63 | - name: Get changes 64 | if: github.event_name == 'workflow_dispatch' 65 | run: | 66 | pattern='0,/[0-9]\+\.[0-9]\+\.[0-9]\+/d;/[0-9]\+\.[0-9]\+\.[0-9]\+/Q' 67 | sed $pattern CHANGELOG.md | head -n -1 | tail -n +2 > RELEASE.md 68 | cat RELEASE.md 69 | 70 | - name: Generate token 71 | if: success() && github.event_name == 'push' 72 | id: generate-token 73 | uses: tibdex/github-app-token@v2 74 | with: 75 | app_id: ${{ secrets.APP_ID }} 76 | private_key: ${{ secrets.APP_PRIVATE_KEY }} 77 | 78 | - name: Create release 79 | if: success() && github.event_name == 'push' 80 | uses: softprops/action-gh-release@v2 81 | with: 82 | token: ${{ steps.generate-token.outputs.token }} 83 | body_path: RELEASE.md 84 | prerelease: ${{ contains(github.ref, '-') }} 85 | files: dist/* 86 | 87 | pypi: 88 | name: PyPI 89 | runs-on: ubuntu-latest 90 | needs: build 91 | 92 | steps: 93 | - name: Checkout 94 | uses: actions/checkout@v4 95 | 96 | - name: Download build artifacts 97 | uses: actions/download-artifact@v4 98 | with: 99 | name: poetry-build 100 | path: dist 101 | 102 | - name: Set up Python 103 | uses: actions/setup-python@v5 104 | with: 105 | python-version: 3.9 106 | 107 | - name: Install Poetry 108 | uses: snok/install-poetry@v1 109 | with: 110 | virtualenvs-create: true 111 | virtualenvs-in-project: true 112 | 113 | - name: Publish to PyPI 114 | if: success() && github.event_name == 'push' 115 | run: | 116 | poetry config pypi-token.pypi ${{ secrets.PYPI_TOKEN }} 117 | poetry publish 118 | 119 | - name: Publish to TestPyPI 120 | if: success() && github.event_name == 'workflow_dispatch' 121 | run: | 122 | poetry config repositories.testpypi https://test.pypi.org/legacy/ 123 | poetry config pypi-token.testpypi ${{ secrets.TEST_PYPI_TOKEN }} 124 | poetry publish -r testpypi 125 | -------------------------------------------------------------------------------- /.github/workflows/security.yml: -------------------------------------------------------------------------------- 1 | name: Security 2 | 3 | on: 4 | push: 5 | branches: 6 | - main 7 | paths-ignore: 8 | - '**.md' 9 | # every Sunday at midnight 10 | schedule: 11 | - cron: '0 0 * * 0' 12 | 13 | permissions: 14 | actions: read 15 | contents: read 16 | security-events: write 17 | 18 | jobs: 19 | codeql: 20 | name: CodeQL 21 | runs-on: ubuntu-latest 22 | 23 | steps: 24 | - name: Checkout 25 | uses: actions/checkout@v4 26 | 27 | - name: Initialize CodeQL 28 | uses: github/codeql-action/init@v3 29 | with: 30 | languages: python 31 | 32 | - name: Run CodeQL 33 | uses: github/codeql-action/analyze@v3 34 | 35 | ossar: 36 | name: OSSAR 37 | runs-on: windows-latest 38 | 39 | steps: 40 | - name: Checkout 41 | uses: actions/checkout@v4 42 | 43 | - name: Run OSSAR 44 | id: ossar 45 | uses: github/ossar-action@v1 46 | 47 | - name: Upload OSSAR results 48 | if: always() 49 | uses: github/codeql-action/upload-sarif@v3 50 | with: 51 | sarif_file: ${{ steps.ossar.outputs.sarifFile }} 52 | 53 | semgrep: 54 | name: Semgrep 55 | runs-on: ubuntu-latest 56 | container: 57 | image: semgrep/semgrep 58 | 59 | steps: 60 | - name: Checkout 61 | uses: actions/checkout@v4 62 | 63 | - name: Run Semgrep 64 | run: semgrep scan --config auto --sarif > semgrep.sarif 65 | 66 | - name: Upload Semgrep results 67 | if: always() 68 | uses: github/codeql-action/upload-sarif@v3 69 | with: 70 | sarif_file: semgrep.sarif 71 | -------------------------------------------------------------------------------- /.github/workflows/test.yml: -------------------------------------------------------------------------------- 1 | name: Tests 2 | 3 | on: 4 | push: 5 | branches: 6 | - main 7 | paths-ignore: 8 | - 'docs/**' 9 | - '**.md' 10 | pull_request: 11 | paths-ignore: 12 | - 'docs/**' 13 | - '**.md' 14 | 15 | jobs: 16 | test: 17 | name: ${{ matrix.os }} / ${{ matrix.python-version }} 18 | runs-on: ${{ matrix.os }}-latest 19 | defaults: 20 | run: 21 | shell: bash 22 | 23 | strategy: 24 | fail-fast: false 25 | matrix: 26 | os: [Ubuntu, macOS, Windows] 27 | python-version: [3.8, 3.9] 28 | 29 | steps: 30 | - name: Checkout 31 | uses: actions/checkout@v4 32 | 33 | - name: Set up TorchTS 34 | uses: ./.github/actions/setup-torchts 35 | with: 36 | python-version: ${{ matrix.python-version }} 37 | 38 | - name: Run tests 39 | run: poetry run pytest tests/ --cov=torchts 40 | 41 | - name: Generate coverage report 42 | run: poetry run coverage xml 43 | 44 | - name: Upload coverage report 45 | if: success() 46 | uses: codecov/codecov-action@v4 47 | with: 48 | file: ./coverage.xml 49 | fail_ci_if_error: true 50 | token: ${{ secrets.CODECOV_TOKEN }} 51 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # macOS 2 | .DS_Store 3 | 4 | # Python byte code 5 | *.pyc 6 | 7 | # Jupyter notebook checkpoints 8 | .ipynb_checkpoints/ 9 | 10 | # PyTorch Lightning logs 11 | lightning_logs/ 12 | 13 | # pytest coverage files 14 | .coverage* 15 | 16 | # build artifacts 17 | build/ 18 | dist/ 19 | 20 | # data files 21 | *.h5 22 | *.npz 23 | *.pkl 24 | 25 | # vscode 26 | .vscode 27 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | ci: 2 | autofix_commit_msg: Add pre-commit fixes 3 | autofix_prs: true 4 | autoupdate_commit_msg: Update pre-commit hooks 5 | autoupdate_schedule: monthly 6 | 7 | repos: 8 | - repo: https://github.com/psf/black 9 | rev: 24.4.0 10 | hooks: 11 | - id: black 12 | 13 | - repo: https://github.com/asottile/pyupgrade 14 | rev: v3.15.2 15 | hooks: 16 | - id: pyupgrade 17 | args: [--py36-plus] 18 | 19 | - repo: https://github.com/PyCQA/isort 20 | rev: 5.13.2 21 | hooks: 22 | - id: isort 23 | additional_dependencies: [toml] 24 | 25 | - repo: https://github.com/flakeheaven/flakeheaven 26 | rev: 3.3.0 27 | hooks: 28 | - id: flakeheaven 29 | additional_dependencies: 30 | - flake8-bugbear 31 | - flake8-comprehensions 32 | 33 | - repo: https://github.com/pre-commit/pre-commit-hooks 34 | rev: v4.6.0 35 | hooks: 36 | - id: end-of-file-fixer 37 | - id: mixed-line-ending 38 | - id: trailing-whitespace 39 | 40 | - repo: https://github.com/Lucas-C/pre-commit-hooks 41 | rev: v1.5.5 42 | hooks: 43 | - id: forbid-crlf 44 | exclude: docs/make.bat 45 | - id: forbid-tabs 46 | exclude: (?i)docs/make* 47 | -------------------------------------------------------------------------------- /CHANGELOG.md: -------------------------------------------------------------------------------- 1 | # Change Log 2 | 3 | ## [Unreleased] 4 | 5 | ### Added 6 | 7 | - Added conda-forge README badge. ([#149](https://github.com/Rose-STL-Lab/torchTS/pull/149)) 8 | - Added pre-commit.ci config and README badge. ([#156](https://github.com/Rose-STL-Lab/torchTS/pull/156)) 9 | - Added quantile loss function for uncertainty quantification. ([#168](https://github.com/Rose-STL-Lab/torchTS/pull/168)) 10 | - Added input to base model constructor for loss function arguments. ([#168](https://github.com/Rose-STL-Lab/torchTS/pull/168)) 11 | - Added pytest-mock to dev dependencies. ([#168](https://github.com/Rose-STL-Lab/torchTS/pull/168)) 12 | - Added website descriptions for new features. ([#165](https://github.com/Rose-STL-Lab/torchTS/pull/165), [#169](https://github.com/Rose-STL-Lab/torchTS/pull/169)) 13 | - Added mean interval score loss function. ([#188](https://github.com/Rose-STL-Lab/torchTS/pull/188)) 14 | - Added API documentation to website. ([#206](https://github.com/Rose-STL-Lab/torchTS/pull/206), [#237](https://github.com/Rose-STL-Lab/torchTS/pull/237), [#238](https://github.com/Rose-STL-Lab/torchTS/pull/238)) 15 | - Added ODE solver and examples. ([#134](https://github.com/Rose-STL-Lab/torchTS/pull/134)) 16 | 17 | ### Changed 18 | 19 | - Updated documentation website. ([#125](https://github.com/Rose-STL-Lab/torchTS/pull/125)) 20 | - Replaced loop with list comprehension. ([#148](https://github.com/Rose-STL-Lab/torchTS/pull/148)) 21 | - Expanded automatic pull request labeling. ([#154](https://github.com/Rose-STL-Lab/torchTS/pull/154), [#204](https://github.com/Rose-STL-Lab/torchTS/pull/204)) 22 | - Expanded gitignore patterns. ([#155](https://github.com/Rose-STL-Lab/torchTS/pull/155)) 23 | - Updated flakehell pre-commit hook. ([#177](https://github.com/Rose-STL-Lab/torchTS/pull/177)) 24 | - Removed pull requests from security workflow runs. ([#185](https://github.com/Rose-STL-Lab/torchTS/pull/185)) 25 | - Switched from flakehell to flakeheaven. ([#203](https://github.com/Rose-STL-Lab/torchTS/pull/203)) 26 | - Removed pre-commit actions. ([#224](https://github.com/Rose-STL-Lab/torchTS/pull/224)) 27 | 28 | ### Fixed 29 | 30 | - Fixed equation parentheses in spatiotemporal documentation. ([#153](https://github.com/Rose-STL-Lab/torchTS/pull/153)) 31 | 32 | ## [0.1.1] - 2021-08-31 33 | 34 | This patch release sets dependency requirements for a `conda` installation. The original requirements were too strict for [conda-forge](https://conda-forge.org/). 35 | 36 | ### Added 37 | 38 | - Added pre-commit to dev dependencies. ([#127](https://github.com/Rose-STL-Lab/torchTS/pull/127)) 39 | 40 | ### Changed 41 | 42 | - Changed CI workflows to run pre-commit with poetry. ([#131](https://github.com/Rose-STL-Lab/torchTS/pull/131)) 43 | - Moved common workflow steps to a composite action. ([#132](https://github.com/Rose-STL-Lab/torchTS/pull/132)) 44 | - Updated pre-commit hooks. ([#133](https://github.com/Rose-STL-Lab/torchTS/pull/133), [#135](https://github.com/Rose-STL-Lab/torchTS/pull/135)) 45 | - Relaxed dependency requirements. ([#139](https://github.com/Rose-STL-Lab/torchTS/pull/139)) 46 | 47 | ### Fixed 48 | 49 | - Fixed change log links. ([#126](https://github.com/Rose-STL-Lab/torchTS/pull/126), [#128](https://github.com/Rose-STL-Lab/torchTS/pull/128)) 50 | - Fixed contributing file link. ([#137](https://github.com/Rose-STL-Lab/torchTS/pull/137)) 51 | - Fixed Sphinx config metadata. ([#138](https://github.com/Rose-STL-Lab/torchTS/pull/138)) 52 | 53 | ## [0.1.0] - 2021-08-16 54 | 55 | Initial release 56 | 57 | [unreleased]: https://github.com/Rose-STL-Lab/torchTS/compare/v0.1.1...main 58 | [0.1.1]: https://github.com/Rose-STL-Lab/torchTS/releases/tag/v0.1.1 59 | [0.1.0]: https://github.com/Rose-STL-Lab/torchTS/releases/tag/v0.1.0 60 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to TorchTS 2 | 3 | :wave: Welcome to TorchTS! Thank you for showing interest and taking the time to contribute. The TorchTS team would love to have your contribution. 4 | 5 | The following is a set of guidelines for contributing to TorchTS on GitHub. Please read carefully, but note that these are mostly guidelines, not rules. Use your best judgement, and feel free to propose changes to this document in a pull request. 6 | 7 | Table of contents: 8 | 9 | - [Issues](#issues) 10 | - [Reporting Bugs](#reporting-bugs) 11 | - [Before submitting a bug report](#before-submitting-a-bug-report) 12 | - [How do I submit a bug report?](#how-do-i-submit-a-bug-report) 13 | - [Suggesting Enhancements](#suggesting-enhancements) 14 | - [Before submitting an enhancement suggestion](#before-submitting-an-enhancement-suggestion) 15 | - [How do I submit an enhancement suggestion?](#how-do-i-submit-an-enhancement-suggestion) 16 | - [Pull Requests](#pull-requests) 17 | - [Contributing to Code](#contributing-to-code) 18 | - [Picking an issue](#picking-an-issue) 19 | - [Local development](#local-development) 20 | - [Running tests](#running-tests) 21 | - [Contributing to Documentation](#contributing-to-documentation) 22 | - [Creating Pull Requests](#creating-pull-requests) 23 | - [License](#license) 24 | 25 | ## Issues 26 | 27 | ### Reporting Bugs 28 | 29 | This section guides you through submitting a bug report for TorchTS. Following these guidelines helps maintainers and the community understand your report, reproduce the behavior, and find related reports. 30 | 31 | #### Before submitting a bug report 32 | 33 | Before submitting bug reports, please search the existing issues on the [issue tracker](https://github.com/Rose-STL-Lab/torchTS/issues) to verify your issue has not already been submitted. Issues pertaining to bugs are usually marked with the [bug](https://github.com/Rose-STL-Lab/torchTS/issues?q=is%3Aissue+label%3Abug) label. 34 | 35 | > **Note:** If you find a **Closed** issue that seems to be the same thing you are experiencing, open a new issue and include a link to the original issue in the body of your new one. 36 | 37 | #### How do I submit a bug report? 38 | 39 | Bugs are tracked on the [issue tracker](https://github.com/Rose-STL-Lab/torchTS/issues) where you can create a new one. When creating a bug report, please include as many details as possible. Explain the problem and include additional details to help maintainers reproduce the problem: 40 | 41 | - **Use a clear and descriptive title** for the issue to identify the problem. 42 | - **Describe the exact steps which reproduce the problem** in as many details as possible. 43 | - **Provide specific examples to demonstrate the steps to reproduce the issue**. Include links to files or GitHub projects, or copy-paste-able snippets, which you use in those examples. 44 | - **Describe the behavior you observed after following the steps** and point out what exactly is the problem with that behavior. 45 | - **Explain which behavior you expected to see instead and why.** 46 | 47 | Provide additional context by answering these questions: 48 | 49 | - **Did the problem start happening recently** (e.g. after updating to a new version of TorchTS) or has this always been a problem? 50 | - If the problem started happening recently, **can you reproduce the problem in an older version of TorchTS?** What is the most recent version in which the problem does not happen? 51 | - **Can you reliably reproduce the issue?** If not, provide details about how often the problem happens and under what conditions it normally happens. 52 | 53 | Include details about your configuration and environment: 54 | 55 | - **Which version of TorchTS are you using?** 56 | - **Which version of Python are you using?** 57 | - **Which OS type and version are you using?** 58 | 59 | ### Suggesting Enhancements 60 | 61 | This section guides you through submitting a suggestion for enhancements to TorchTS, including completely new features and minor improvements to existing functionality. Following these guidelines helps maintainers and the community understand your suggestion and find related suggestions. 62 | 63 | #### Before submitting an enhancement suggestion 64 | 65 | Before submitting enhancement suggestions, please search the existing issues on the [issue tracker](https://github.com/Rose-STL-Lab/torchTS/issues) to verify your issue has not already been submitted. Issues pertaining to enhancements are usually marked with the [enhancement](https://github.com/Rose-STL-Lab/torchTS/issues?q=is%3Aissue+label%3Aenhancement) label. 66 | 67 | > **Note:** If you find a **Closed** issue that seems to be the same thing you are suggesting, please read the conversation to learn why it was not incorporated. 68 | 69 | #### How do I submit an enhancement suggestion? 70 | 71 | Enhancement suggestions are tracked on the [issue tracker](https://github.com/Rose-STL-Lab/torchTS/issues) where you can create a new one. When creating an enhancement suggestion, please include as many details as possible and provide the following information: 72 | 73 | - **Use a clear and descriptive title** for the issue to identify the suggestion. 74 | - **Provide a step-by-step description of the suggested enhancement** in as many details as possible. 75 | - **Provide specific examples to demonstrate the steps**. 76 | - **Describe the current behavior** and **explain which behavior you expected to see instead** and why. 77 | 78 | ## Pull Requests 79 | 80 | ### Contributing to Code 81 | 82 | #### Picking an issue 83 | 84 | If you would like to take on an open issue, feel free to comment on it in the [issue tracker](https://github.com/Rose-STL-Lab/torchTS/issues). We are more than happy to discuss solutions to open issues. If you are particularly adventurous, consider addressing an issue labeled [help wanted](https://github.com/Rose-STL-Lab/torchTS/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22). 85 | 86 | > **Note:** If you are a first time contributor and are looking for an issue to take on, you might want to look through the issues labeled [good first issue](https://github.com/Rose-STL-Lab/torchTS/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). 87 | 88 | #### Local development 89 | 90 | You will need the TorchTS source to start contributing to the codebase. Refer to the [documentation](https://rose-stl-lab.github.io/torchTS/docs/) to start using TorchTS. You will first need to clone the repository using `git` and place yourself in the new local directory: 91 | 92 | ```bash 93 | git clone git@github.com:Rose-STL-Lab/torchTS.git 94 | cd torchTS 95 | ``` 96 | 97 | > **Note:** We recommend that you use a personal [fork](https://docs.github.com/en/get-started/quickstart/fork-a-repo) for this step. If you are new to GitHub collaboration, you can refer to the [Forking Projects Guide](https://guides.github.com/activities/forking/). 98 | 99 | TorchTS uses [Poetry](https://python-poetry.org/) for dependency management and we recommend you do the same when contributing to TorchTS. Refer to the [installation instructions](https://python-poetry.org/docs/#installation) to determine how to install Poetry on your system. Once installed, you can create a virtual environment and install TorchTS including all dependencies with the `install` command: 100 | 101 | ```bash 102 | poetry install 103 | ``` 104 | 105 | > **Note:** Poetry uses the active Python installation to create the virtual environment. You can determine your current Python version with `python --version` and find the location of your Python executable with `which python`. 106 | 107 | #### Running tests 108 | 109 | Once your changes are complete, make sure that the tests pass on your machine: 110 | 111 | ```bash 112 | poetry run pytest tests/ 113 | ``` 114 | 115 | > **Note:** Your code must always be accompanied by corresponding tests. Your pull request **will not be merged** if tests are not present. 116 | 117 | TorchTS uses [pre-commit](https://pre-commit.com/) to run a series of checks that ensure all files adhere to a consistent code style and satisfy desired coding standards. These include [black](https://github.com/psf/black) and [pyupgrade](https://github.com/asottile/pyupgrade) to format code, [isort](https://github.com/PyCQA/isort) to sort import statements, and [Flake8](https://github.com/PyCQA/flake8) to check for common coding errors. 118 | 119 | To make sure that you do not accidentally commit code that does not follow the coding style, you can run these checks with the following command: 120 | 121 | ```bash 122 | poetry run pre-commit run --all-files 123 | ``` 124 | 125 | > **Note:** Many of the pre-commit hooks modify your code if necessary. If pre-commit fails, it will oftentimes pass when run a second time. 126 | 127 | Failure to satisfy these checks will cause the CI to fail and your pull request **will not be merged**. 128 | 129 | ### Contributing to Documentation 130 | 131 | One of the simplest ways to get started contributing to a project is through improving documentation. You can help by adding missing sections, editing the existing content so it is more accessible, or creating new content (tutorials, FAQs, etc). 132 | 133 | Issues pertaining to the documentation are usually marked with the [documentation](https://github.com/Rose-STL-Lab/torchTS/issues?q=is%3Aissue+is%3Aopen+label%3Adocumentation) label. 134 | 135 | ### Creating Pull Requests 136 | 137 | - Be sure that your pull request contains tests that cover the changed or added code. 138 | - If your changes warrant a documentation change, the pull request must also update the documentation. 139 | 140 | > **Note:** Make sure your branch is [rebased](https://docs.github.com/en/get-started/using-git/about-git-rebase) against the latest `main` branch. A maintainer might ask you to ensure the branch is up-to-date prior to merging your pull request if changes have conflicts. 141 | 142 | All pull requests, unless otherwise instructed, need to be first accepted into the `main` branch. 143 | 144 | ## License 145 | 146 | By contributing to TorchTS, you agree that your contributions will be licensed under the [LICENSE](LICENSE) file in the root directory of this source tree. 147 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2020-present UCSD STL Lab 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy of 6 | this software and associated documentation files (the "Software"), to deal in 7 | the Software without restriction, including without limitation the rights to 8 | use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies 9 | of the Software, and to permit persons to whom the Software is furnished to do 10 | so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS 17 | FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR 18 | COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER 19 | IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN 20 | CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. 21 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | TorchTS Logo 3 | 4 | 5 | --- 6 | 7 | [![Tests](https://github.com/Rose-STL-Lab/torchTS/actions/workflows/test.yml/badge.svg?branch=main)](https://github.com/Rose-STL-Lab/torchTS/actions/workflows/test.yml) 8 | [![Docs](https://github.com/Rose-STL-Lab/torchTS/actions/workflows/docs.yml/badge.svg?branch=main)](https://github.com/Rose-STL-Lab/torchTS/actions/workflows/docs.yml) 9 | [![pre-commit.ci](https://results.pre-commit.ci/badge/github/Rose-STL-Lab/torchTS/main.svg)](https://results.pre-commit.ci/latest/github/Rose-STL-Lab/torchTS/main) 10 | [![Codecov](https://img.shields.io/codecov/c/github/Rose-STL-Lab/torchTS?label=Coverage&logo=codecov)](https://app.codecov.io/gh/Rose-STL-Lab/torchTS) 11 | [![PyPI](https://img.shields.io/pypi/v/torchts?label=PyPI&logo=python)](https://pypi.org/project/torchts) 12 | [![Conda](https://img.shields.io/conda/v/conda-forge/torchts?label=Conda&logo=anaconda)](https://anaconda.org/conda-forge/torchts) 13 | [![License](https://img.shields.io/github/license/Rose-STL-Lab/torchTS?label=License)](LICENSE) 14 | 15 | TorchTS is a PyTorch-based library for time series data. 16 | 17 | ***Currently under active development!*** 18 | 19 | #### Why Time Series? 20 | 21 | Time series data modeling has broad significance in public health, finance and engineering. Traditional time series methods from statistics often rely on strong modeling assumptions, or are computationally expensive. Given the rise of large-scale sensing data and significant advances in deep learning, the goal of the project is to develop an efficient and user-friendly deep learning library that would benefit the entire research community and beyond. 22 | 23 | #### Why TorchTS? 24 | 25 | Existing time series analysis libraries include [statsmodels](https://www.statsmodels.org/stable/index.html) and [sktime](https://github.com/alan-turing-institute/sktime). However, these libraries only include traditional statistics tools such as ARMA or ARIMA, which do not have the state-of-the-art forecasting tools based on deep learning. [GluonTS](https://ts.gluon.ai/) is an open-source time series library developed by Amazon AWS, but is based on MXNet. [Pyro](https://pyro.ai/) is a probabilistic programming framework based on PyTorch, but is not focused on time series forecasting. 26 | 27 | ## Installation 28 | 29 | ### Installation Requirements 30 | 31 | TorchTS supports Python 3.8+ and has the following dependencies: 32 | 33 | - [PyTorch](https://pytorch.org/) 34 | - [PyTorch Lightning](https://pytorchlightning.ai/) 35 | - [SciPy](https://www.scipy.org/) 36 | 37 | ### Installing the latest release 38 | 39 | The latest release of TorchTS is easily installed either via `pip`: 40 | 41 | ```bash 42 | pip install torchts 43 | ``` 44 | 45 | or via [conda](https://docs.conda.io/projects/conda/) from the [conda-forge](https://conda-forge.org/) channel: 46 | 47 | ```bash 48 | conda install -c conda-forge torchts 49 | ``` 50 | 51 | You can customize your PyTorch installation (i.e. CUDA version, CPU only option) 52 | by following the [PyTorch installation instructions](https://pytorch.org/get-started/locally/). 53 | 54 | ***Important note for MacOS users:*** 55 | 56 | - Make sure your PyTorch build is linked against MKL (the non-optimized version 57 | of TorchTS can be up to an order of magnitude slower in some settings). 58 | Setting this up manually on MacOS can be tricky - to ensure this works properly, 59 | please follow the [PyTorch installation instructions](https://pytorch.org/get-started/locally/). 60 | - If you need CUDA on MacOS, you will need to build PyTorch from source. Please 61 | consult the PyTorch installation instructions above. 62 | 63 | ## Getting Started 64 | 65 | Check out our [documentation](https://rose-stl-lab.github.io/torchTS/) and 66 | [tutorials](https://rose-stl-lab.github.io/torchTS/tutorials) (coming soon). 67 | 68 | ## Citing TorchTS 69 | 70 | If you use TorchTS, please cite the following paper (coming soon): 71 | 72 | > [TorchTS: A Framework for Efficient Time Series Modeling](TBD) 73 | 74 | ```bibtex 75 | @inproceedings{TBD, 76 | title={{TorchTS: A Framework for Efficient Time Series Modeling}}, 77 | author={TBD}, 78 | booktitle = {TBD}, 79 | year={TBD}, 80 | url = {TBD} 81 | } 82 | ``` 83 | 84 | See [here](https://rose-stl-lab.github.io/torchTS/papers) (coming soon) for a selection of peer-reviewed papers that either build off of TorchTS or were integrated into TorchTS. 85 | 86 | ## Contributing 87 | 88 | Interested in contributing to TorchTS? Please see the [contributing guide](CONTRIBUTING.md) to learn how to help out. 89 | 90 | ## License 91 | 92 | TorchTS is [MIT licensed](LICENSE). 93 | -------------------------------------------------------------------------------- /codecov.yml: -------------------------------------------------------------------------------- 1 | coverage: 2 | status: 3 | project: 4 | default: 5 | informational: true 6 | patch: 7 | default: 8 | informational: true 9 | -------------------------------------------------------------------------------- /docs/Makefile: -------------------------------------------------------------------------------- 1 | # Minimal makefile for Sphinx documentation 2 | # 3 | 4 | # You can set these variables from the command line, and also 5 | # from the environment for the first two. 6 | SPHINXOPTS ?= 7 | SPHINXBUILD ?= sphinx-build 8 | SOURCEDIR = source 9 | BUILDDIR = build 10 | 11 | # Put it first so that "make" without argument is like "make help". 12 | help: 13 | @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 14 | 15 | .PHONY: help Makefile 16 | 17 | # Catch-all target: route all unknown targets to Sphinx using the new 18 | # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). 19 | %: Makefile 20 | @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) 21 | -------------------------------------------------------------------------------- /docs/make.bat: -------------------------------------------------------------------------------- 1 | @ECHO OFF 2 | 3 | pushd %~dp0 4 | 5 | REM Command file for Sphinx documentation 6 | 7 | if "%SPHINXBUILD%" == "" ( 8 | set SPHINXBUILD=sphinx-build 9 | ) 10 | set SOURCEDIR=source 11 | set BUILDDIR=build 12 | 13 | if "%1" == "" goto help 14 | 15 | %SPHINXBUILD% >NUL 2>NUL 16 | if errorlevel 9009 ( 17 | echo. 18 | echo.The 'sphinx-build' command was not found. Make sure you have Sphinx 19 | echo.installed, then set the SPHINXBUILD environment variable to point 20 | echo.to the full path of the 'sphinx-build' executable. Alternatively you 21 | echo.may add the Sphinx directory to PATH. 22 | echo. 23 | echo.If you don't have Sphinx installed, grab it from 24 | echo.http://sphinx-doc.org/ 25 | exit /b 1 26 | ) 27 | 28 | %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 29 | goto end 30 | 31 | :help 32 | %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% 33 | 34 | :end 35 | popd 36 | -------------------------------------------------------------------------------- /docs/source/_static/css/custom.css: -------------------------------------------------------------------------------- 1 | .tutorials-header .header-logo { 2 | background-image: url("../images/torchTS_logo.png"); 3 | background-size: cover; 4 | background-repeat: no-repeat; 5 | background-position: 50% 50%; 6 | width: 110px; 7 | } 8 | 9 | @media only screen and (max-width: 600px) { 10 | .tutorials-header .header-logo { 11 | width: 50px; 12 | } 13 | } 14 | 15 | span.pre { 16 | background: inherit; 17 | } 18 | 19 | code span.pre { 20 | color: #262626; 21 | } 22 | 23 | article.pytorch-article .class .method dt { 24 | font-weight: 500; 25 | } 26 | -------------------------------------------------------------------------------- /docs/source/_static/images/favicon.ico: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/docs/source/_static/images/favicon.ico -------------------------------------------------------------------------------- /docs/source/_static/images/torchTS_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/docs/source/_static/images/torchTS_logo.png -------------------------------------------------------------------------------- /docs/source/_templates/theme_variables.jinja: -------------------------------------------------------------------------------- 1 | {%- set external_urls = { 2 | 'github': 'https://github.com/Rose-STL-Lab/torchTS', 3 | 'home': 'https://rose-stl-lab.github.io/torchTS/api/', 4 | 'get_started': ' ', 5 | 'blog': 'https://github.com/Rose-STL-Lab/torchTS', 6 | 'resources': 'https://github.com/Rose-STL-Lab/torchTS', 7 | 'docs': 'https://rose-stl-lab.github.io/torchTS/docs/', 8 | 'twitter': 'https://twitter.com/', 9 | 'discuss': 'https://discuss.pytorch.org', 10 | 'tutorials': 'https://github.com/Rose-STL-Lab/torchTS/docs', 11 | 'previous_pytorch_versions': 'https://pytorch-lightning.rtfd.io/en/latest/', 12 | } 13 | -%} 14 | -------------------------------------------------------------------------------- /docs/source/conf.py: -------------------------------------------------------------------------------- 1 | # Configuration file for the Sphinx documentation builder. 2 | # 3 | # This file only contains a selection of the most common options. For a full 4 | # list see the documentation: 5 | # https://www.sphinx-doc.org/en/master/usage/configuration.html 6 | 7 | # -- Path setup -------------------------------------------------------------- 8 | 9 | # If extensions (or modules to document with autodoc) are in another directory, 10 | # add these directories to sys.path here. If the directory is relative to the 11 | # documentation root, use os.path.abspath to make it absolute, like shown here. 12 | 13 | import os 14 | import sys 15 | 16 | import asteroid_sphinx_theme 17 | 18 | PATH_HERE = os.path.abspath(os.path.dirname(__file__)) 19 | PATH_ROOT = os.path.join(PATH_HERE, "..", "..") 20 | sys.path.insert(0, os.path.abspath(PATH_ROOT)) 21 | 22 | 23 | # -- Project information ----------------------------------------------------- 24 | 25 | project = "TorchTS" 26 | copyright = "2020-present UCSD STL Lab" 27 | author = "TorchTS Team" 28 | 29 | # The full version, including alpha/beta/rc tags 30 | release = "0.1.1" 31 | 32 | 33 | # -- General configuration --------------------------------------------------- 34 | 35 | # Add any Sphinx extension module names here, as strings. They can be 36 | # extensions coming with Sphinx (named "sphinx.ext.*") or your custom 37 | # ones. 38 | extensions = [ 39 | "sphinx.ext.autodoc", 40 | "sphinx.ext.autosummary", 41 | "sphinx.ext.doctest", 42 | "sphinx.ext.intersphinx", 43 | "sphinx.ext.todo", 44 | "sphinx.ext.coverage", 45 | "sphinx.ext.napoleon", 46 | "sphinx.ext.viewcode", 47 | "sphinx.ext.githubpages", 48 | ] 49 | 50 | # Add any paths that contain templates here, relative to this directory. 51 | templates_path = ["_templates"] 52 | 53 | # List of patterns, relative to source directory, that match files and 54 | # directories to ignore when looking for source files. 55 | # This pattern also affects html_static_path and html_extra_path. 56 | exclude_patterns = [] 57 | 58 | # The name of the Pygments (syntax highlighting) style to use. 59 | pygments_style = None 60 | 61 | 62 | # -- Extension configuration ------------------------------------------------- 63 | 64 | # Napoleon config 65 | napoleon_use_ivar = True 66 | napoleon_use_rtype = False 67 | 68 | # Autodoc config 69 | autodoc_inherit_docstrings = False 70 | autodoc_default_options = {"members": True, "show-inheritance": True} 71 | 72 | # Intersphinx config 73 | intersphinx_mapping = { 74 | "python": ("https://docs.python.org/3/", None), 75 | "torch": ("https://pytorch.org/docs/master/", None), 76 | } 77 | 78 | 79 | # -- Options for HTML output ------------------------------------------------- 80 | 81 | # The theme to use for HTML and HTML Help pages. See the documentation for 82 | # a list of builtin themes. 83 | html_theme = "asteroid_sphinx_theme" 84 | html_theme_path = [asteroid_sphinx_theme.get_html_theme_path()] 85 | 86 | # Theme options are theme-specific and customize the look and feel of a theme 87 | # further. For a list of options available for each theme, see the 88 | # documentation. 89 | html_theme_options = { 90 | "pytorch_project": "tutorials", 91 | "canonical_url": "https://rose-stl-lab.github.io/torchTS/api/", 92 | "collapse_navigation": False, 93 | "display_version": True, 94 | "logo": "_static/images/torchTS_logo.png", 95 | "logo_only": True, 96 | } 97 | 98 | html_logo = "_static/images/torchTS_logo.png" 99 | 100 | # html_context = { 101 | # "display_github": True, 102 | # "github_user": "Rose-STL-Lab", 103 | # "github_repo": "torchTS", 104 | # "github_version": "master", 105 | # "conf_py_path": "/docs/", # needs leading and trailing slashes! 106 | # } 107 | 108 | # Add any paths that contain custom static files (such as style sheets) here, 109 | # relative to this directory. They are copied after the builtin static files, 110 | # so a file named "default.css" will overwrite the builtin "default.css". 111 | html_static_path = ["_static"] 112 | 113 | html_css_files = [ 114 | "css/custom.css", 115 | ] 116 | -------------------------------------------------------------------------------- /docs/source/index.rst: -------------------------------------------------------------------------------- 1 | .. TorchTS documentation master file, created by 2 | sphinx-quickstart on Tue Dec 22 18:49:00 2020. 3 | You can adapt this file completely to your liking, but it should at least 4 | contain the root `toctree` directive. 5 | 6 | Welcome to TorchTS's documentation! 7 | =================================== 8 | 9 | .. toctree:: 10 | :maxdepth: 2 11 | :caption: Contents: 12 | 13 | modules 14 | torchts 15 | 16 | 17 | 18 | Indices and tables 19 | ================== 20 | 21 | * :ref:`genindex` 22 | * :ref:`modindex` 23 | * :ref:`search` 24 | -------------------------------------------------------------------------------- /docs/source/modules.rst: -------------------------------------------------------------------------------- 1 | torchts 2 | ======= 3 | 4 | .. toctree:: 5 | :maxdepth: 4 6 | 7 | torchts 8 | -------------------------------------------------------------------------------- /docs/source/torchts.rst: -------------------------------------------------------------------------------- 1 | torchts package 2 | =============== 3 | 4 | Submodules 5 | ---------- 6 | 7 | Base classes 8 | ------------ 9 | 10 | .. automodule:: torchts.nn.model 11 | 12 | Module contents 13 | --------------- 14 | 15 | .. automodule:: torchts 16 | :members: 17 | :undoc-members: 18 | :show-inheritance: 19 | -------------------------------------------------------------------------------- /examples/dcrnn/README.md: -------------------------------------------------------------------------------- 1 | # This tutorial serves as a basic example of training the DCRNN model. 2 | 3 | ### Download sample data to the project directory [here](https://drive.google.com/drive/folders/1VB5OGQudoEOHCf0Y-J0wuDByU8OCcsMY?usp=sharing). 4 | 5 | 6 |

7 | The traffic dataset stated above is obtained from CalTrans monitoring system and is used to predict traffic at California's road network. 8 | 9 | Major requirements include numpy , torch and pytorch-lightning and can be found in the requirements.txt 10 | The tutorial code can be found in main.py 11 |

12 | 13 | ``` 14 | pip install -r requirements.txt 15 | 16 | # Run tutorial 17 | python main.py 18 | ``` 19 | -------------------------------------------------------------------------------- /examples/dcrnn/main.py: -------------------------------------------------------------------------------- 1 | import time 2 | 3 | import numpy as np 4 | import torch.optim 5 | 6 | from torchts.nn.models.dcrnn import DCRNN 7 | from torchts.utils import data as utils 8 | 9 | model_config = { 10 | "cl_decay_steps": 2000, 11 | "filter_type": "dual_random_walk", 12 | "horizon": 12, 13 | "seq_len": 12, 14 | "input_dim": 2, 15 | "max_diffusion_step": 2, 16 | "num_layers": 2, 17 | "output_dim": 2, 18 | "use_curriculum_learning": True, 19 | } 20 | 21 | optimizer_args = {"lr": 0.01} 22 | 23 | # Code to retrieve the graph in the form of an adjacency matrix. 24 | # This corresponds to the distance between 2 traffic sensors in a traffic network. 25 | # For other applications it can mean anything that defines the adjacency between nodes 26 | # eg. distance between airports of different cities when predicting 27 | # covid infection rate. 28 | 29 | graph_pkl_filename = "" 30 | 31 | _, _, adj_mx = utils.load_graph_data(graph_pkl_filename) 32 | 33 | num_units = adj_mx.shape[0] 34 | 35 | model_config["num_nodes"] = num_units 36 | 37 | data = np.load( 38 | "" 39 | ) # Absolute path of train, test, val needed. 40 | 41 | 42 | def run(): 43 | model = DCRNN( 44 | adj_mx, 45 | num_units, 46 | optimizer=torch.optim.SGD, 47 | optimizer_args=optimizer_args, 48 | **model_config 49 | ) 50 | start = time.time() 51 | model.fit( 52 | torch.from_numpy(data["x"].astype("float32")), 53 | torch.from_numpy(data["y"].astype("float32")), 54 | max_epochs=10, 55 | batch_size=8, 56 | ) 57 | end = time.time() - start 58 | print("Training time taken %f" % (end - start)) 59 | 60 | 61 | if __name__ == "__main__": 62 | run() 63 | -------------------------------------------------------------------------------- /examples/dcrnn/requirements.txt: -------------------------------------------------------------------------------- 1 | numpy 2 | torch>=1.7.1 3 | pytorch-lightning>=1.3.1 4 | -------------------------------------------------------------------------------- /examples/mis-regression/README.md: -------------------------------------------------------------------------------- 1 | # LSTM with Mean Interval Score Regression 2 | 3 | This notebook provides an example of using a mean interval score loss function with a LSTM network. 4 | 5 | Assuming TorchTS is already installed, the additional dependencies can be installed with `pip`: 6 | 7 | ```bash 8 | pip install -r requirements.txt 9 | ``` 10 | -------------------------------------------------------------------------------- /examples/mis-regression/requirements.txt: -------------------------------------------------------------------------------- 1 | matplotlib 2 | ipykernel 3 | -------------------------------------------------------------------------------- /examples/ode/SEIR/SEIR model with ODESolver.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## SEIR Epidemiologic Model with TorchTS ODE Solver\n", 8 | "\n", 9 | "This example solves a compartmental model used in epidemiology, known as \n", 10 | "[SEIR](https://en.wikipedia.org/wiki/Compartmental_models_in_epidemiology#The_SEIR_model) \n", 11 | "model, using the TorchTS ODE Solver.\n", 12 | "\n", 13 | "The ODE (ordinary differential equation) system, in generic form, is\n", 14 | "\n", 15 | "\\begin{equation*}\n", 16 | " \\frac{d \\mathbf{A}}{d t} = \\mathbf{F}(a_n)\n", 17 | "\\end{equation*}\n", 18 | "\n", 19 | "or in uncollapsed form\n", 20 | "\n", 21 | "\\begin{align*}\n", 22 | "\\frac{d a_1}{d t} =& f_1(a_1, a_2, \\dots a_n) \\\\\n", 23 | "\\frac{d a_2}{d t} =& f_2(a_1, a_2, \\dots a_n) \\\\\n", 24 | "\\vdots \\\\\n", 25 | "\\frac{d a_n}{d t} =& f_n(a_1, a_2, \\dots a_n) \\\\\n", 26 | "\\end{align*}\n", 27 | "\n", 28 | "In the case of the SEIR model, the ODEs are:\n", 29 | "
\n", 30 | "\\begin{align}\n", 31 | "\\frac{d S_t}{dt} &= - \\frac{\\beta_t I_t S_t}{N}, \\\\\n", 32 | "\\frac{d E_t}{dt} &= \\frac{\\beta_t I_t S_t}{N} - \\sigma_t E_t \\\\\n", 33 | "\\frac{d I_t}{dt} &= \\sigma_t E_t - \\gamma_t I_t \\\\\n", 34 | "\\frac{d R_t}{dt} &= \\gamma_t I_t\n", 35 | "\\end{align}\n", 36 | "\n", 37 | "Here, the the compartment $S$ (susceptible population) represents the first variable $a_1$, and $f_1$ is denoted by the right-hand-side of the top equation.\n", 38 | "The coefficients $\\beta$, $\\sigma$ and $\\gamma$ (either constant or time-dependent, still to be implemented) are optimized using PyTorch." 39 | ] 40 | }, 41 | { 42 | "cell_type": "markdown", 43 | "metadata": {}, 44 | "source": [ 45 | "In order to solve various different systems of ODEs, the following quantities must be somehow parameterized and passed to the solver:\n", 46 | "- The equations $f_n$. \n", 47 | "- The coefficients (and a flag denoting whether they are time-dependent)\n", 48 | "- The data used to train the model. \n", 49 | "- An optional output modifier which takes the numerical solution and brings it into a shape consistent with the training data such that the loss can be calculated, as explained below.\n", 50 | "- Other user-controllable parameters including the temporal discretization (time step),\n", 51 | "optimizer, scheduler learning rate and loss function." 52 | ] 53 | }, 54 | { 55 | "cell_type": "markdown", 56 | "metadata": {}, 57 | "source": [ 58 | "### Specification of the variables, parameters and functions\n", 59 | "\n", 60 | "Variables, ODEs and initial values for coefficients are passed to the function during initialization:\n", 61 | "\n", 62 | " ODESolver(inivals, cfuncs, inicoeffs, dt, time-dependent=False, \n", 63 | " solver='Euler', outvar=None)\n", 64 | "\n", 65 | "##### Variables\n", 66 | "For working with the solver it's easier and more intuitive to assign actual variable names to each quantity.\n", 67 | "They are provided as the keys in the dictionary passed to \n", 68 | "the positional argument `inivals`. The values of the dictionary provide the initial values assigned to each quantity.\n", 69 | "For the SEIR model, one could use, for example:\n", 70 | "\n", 71 | " inivals = {\"S\": 0.95, \"E\": 0.,\"I\" 0.05 : ,\"R\": 0}\n", 72 | " \n", 73 | "(Here, the population in each compartments is normalized by the total population size).\n", 74 | "\n", 75 | "##### Functions\n", 76 | "\n", 77 | "A function specifying the right-hand term in each of the system of ODE's is passed to the solver as a dictionary in the\n", 78 | "positional argument `cfuncs`. The equation pertaining to each variable is stored under the key representing the respective variable.\n", 79 | "Each function receives two positional arguments, `cvars` and `coeffs`. These will be dictionaries containing the \n", 80 | "system's current variables and coefficients. As an example, the function describing the ODE for quantity $S$ would be defined as:\n", 81 | "\n", 82 | " def fS(cvar, coeffs):\n", 83 | " return (-coeffs[\"beta\"] * cvar[\"I\"] * cvar[\"S\"])\n", 84 | "\n", 85 | "##### Inicoeffs\n", 86 | "\n", 87 | "Initial values for the coefficients are provided in the dictionary inicoeffs. Each coefficient must be present,\n", 88 | "and the keys in the dictionary passed to the solver must represent the names of the coefficients that will be optimized through data.\n", 89 | "\n", 90 | "In the SEIR example, one could use\n", 91 | " \n", 92 | " inicoeffs={\"beta\": 0.50, \"gamma\": 0.20, \"sigma\": 0.20}\n", 93 | "\n", 94 | "##### Output quantities (and time skip, still ToDo):\n", 95 | "\n", 96 | "By default, the network returns a time-dependent value for every variable and every discrete time step resolved \n", 97 | "during numerical integration. Depending on the model and data, a training value may not be available for quantity.\n", 98 | "For example, only data on the currently infected and susceptible population was typically be available during the Covid-19 pandemic, but not on the exposed population.\n", 99 | "(Alternatively, one might only have data on cumulative reported cases (`cumsum(I)`), not currently infectious cases.\n", 100 | "Handling such cases will require functionaly that is not yet implemented.)\n", 101 | "\n", 102 | "The keyword variable `outvar` designates the names of the output quantities that are present in the data and used \n", 103 | "for computation of the loss. In addition, it indicates the order in which they are present in the training dataset\n", 104 | "(format described below).\n", 105 | "By default, `outvar` is the same as `variables`. In the case of the compartmental model, one would use\n", 106 | "`outvar = [\"S\",\"I\",\"R\"]`, as no data on the exposed population $E$ is available." 107 | ] 108 | }, 109 | { 110 | "cell_type": "markdown", 111 | "metadata": {}, 112 | "source": [ 113 | "### Training the network\n", 114 | "\n", 115 | "The solver is trained using \n", 116 | "\n", 117 | " ODESolver.fit(train_data, num_epochs=100, lr=0.001, optimizer= None, \n", 118 | " scheduler = None, loss_fun=torch.nn.MSELoss()):\n", 119 | "\n", 120 | "The PyTorch tensor `train_data` is assumed to be of the shape `(nt,nvar)`, where `nt` is the number of time steps used for training and `nvar` is the number of output variables (consistent with `len(outvar)`). The sampling interval of the data is expected to be the same as the timestep `dt` passed to the solver during initialization.\n", 121 | "\n", 122 | "By default, the value `None` is passed for the optimizer and scheduler, \n", 123 | "and the network uses \n", 124 | "\n", 125 | " optimizer = torch.optim.Adam(self.coeffs.values(), 0.001) \n", 126 | " scheduler=torch.optim.lr_scheduler.StepLR(optimizer, step_size= 1, gamma=0.95)\n", 127 | " \n", 128 | "To learning rate can be changed using the keyword argument `lr`. If a custom optimizer is provided,\n", 129 | "the optimizer's coded learning rate is used. A warning is issued if the user tries to set both.\n", 130 | "\n", 131 | "### Predicting\n", 132 | "\n", 133 | "Predictions are made using \n", 134 | "\n", 135 | " ODESolver.predict(nt)\n", 136 | " \n", 137 | "Where `nt` represents the total number of time steps in the prediction (starting from the same origin time as used in the training data)." 138 | ] 139 | }, 140 | { 141 | "cell_type": "code", 142 | "execution_count": 1, 143 | "metadata": {}, 144 | "outputs": [ 145 | { 146 | "name": "stdout", 147 | "output_type": "stream", 148 | "text": [ 149 | "cpu\n" 150 | ] 151 | } 152 | ], 153 | "source": [ 154 | "import torch\n", 155 | "import numpy as np\n", 156 | "import torch.nn as nn\n", 157 | "from scipy.integrate import odeint\n", 158 | "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", 159 | "import os\n", 160 | "os.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\n", 161 | "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"4\"\n", 162 | "#device = torch.device(\"cpu\")\n", 163 | "print(device)\n", 164 | "import matplotlib.pyplot as plt\n", 165 | "\n", 166 | "from tqdm import tqdm" 167 | ] 168 | }, 169 | { 170 | "cell_type": "markdown", 171 | "metadata": {}, 172 | "source": [ 173 | "If torchts is properly installed, the module should be in the path already. This code adds the\n", 174 | "module `ode` to the current path assuming the example is called directly from the repository's tree:" 175 | ] 176 | }, 177 | { 178 | "cell_type": "code", 179 | "execution_count": 2, 180 | "metadata": {}, 181 | "outputs": [], 182 | "source": [ 183 | "try:\n", 184 | " from torchts.nn.models import ode\n", 185 | "except ModuleNotFoundError:\n", 186 | " import sys\n", 187 | " sys.path.append(\"../../..\")\n", 188 | " from torchts.nn.models import ode" 189 | ] 190 | }, 191 | { 192 | "cell_type": "markdown", 193 | "metadata": {}, 194 | "source": [ 195 | "Defining all the functions. Here, the coefficient $\\beta$ is assumed to be normalized by the total population $N$ already. Population sizes are assumed normalized by the total population as well." 196 | ] 197 | }, 198 | { 199 | "cell_type": "code", 200 | "execution_count": 3, 201 | "metadata": {}, 202 | "outputs": [], 203 | "source": [ 204 | "def fS(cvar, coeffs):\n", 205 | " return -coeffs[\"beta\"] * cvar[\"I\"] * cvar[\"S\"]\n", 206 | "\n", 207 | "def fE(cvar, coeffs):\n", 208 | " return coeffs[\"beta\"] * cvar[\"I\"] * cvar[\"S\"] - coeffs[\"sigma\"] * cvar[\"E\"]\n", 209 | "\n", 210 | "def fI(cvar, coeffs):\n", 211 | " return coeffs[\"sigma\"] * cvar[\"E\"] - coeffs[\"gamma\"] * cvar[\"I\"]\n", 212 | "\n", 213 | "def fR(cvar, coeffs):\n", 214 | " return coeffs[\"gamma\"] * cvar[\"I\"]\n", 215 | "\n", 216 | "cfuncs={\"S\": fS, \"E\": fE, \"I\": fI, \"R\": fR}" 217 | ] 218 | }, 219 | { 220 | "cell_type": "markdown", 221 | "metadata": {}, 222 | "source": [ 223 | "The training and validation data is loaded from the provided `PyTorch` file and normalized:" 224 | ] 225 | }, 226 | { 227 | "cell_type": "code", 228 | "execution_count": 4, 229 | "metadata": {}, 230 | "outputs": [ 231 | { 232 | "name": "stdout", 233 | "output_type": "stream", 234 | "text": [ 235 | "Total population: 3338330\n" 236 | ] 237 | } 238 | ], 239 | "source": [ 240 | "SIR=torch.load(\"SIR_data_SD_county.pt\")\n", 241 | "\n", 242 | "npop=SIR[0,0].numpy().copy()\n", 243 | "print(\"Total population: %d\" % npop)\n", 244 | "SIR[:,:] = SIR[:,:] / torch.tensor(npop)" 245 | ] 246 | }, 247 | { 248 | "cell_type": "markdown", 249 | "metadata": {}, 250 | "source": [ 251 | "Preparing the training data. Here, just a short excerpt of the full time range is used.\n", 252 | "Time-dependent coefficients are needed to fit longer time windows. This is not yet implemented in the ODE library, but in the specific solver for the SEIR model:" 253 | ] 254 | }, 255 | { 256 | "cell_type": "code", 257 | "execution_count": 5, 258 | "metadata": {}, 259 | "outputs": [ 260 | { 261 | "name": "stdout", 262 | "output_type": "stream", 263 | "text": [ 264 | "nt=37\n" 265 | ] 266 | } 267 | ], 268 | "source": [ 269 | "training_data=SIR.float()[350:380,:]\n", 270 | "nt_train=training_data.shape[0]\n", 271 | "test_data=SIR.float()[350:410,:]\n", 272 | "nt=test_data.shape[0]\n", 273 | "print(\"nt=%d\" % nt)" 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": 12, 279 | "metadata": {}, 280 | "outputs": [ 281 | { 282 | "name": "stdout", 283 | "output_type": "stream", 284 | "text": [ 285 | "{'S': tensor(0.9471), 'I': tensor(0.0126), 'R': tensor(0.0403), 'E': tensor(0.0251)}\n" 286 | ] 287 | } 288 | ], 289 | "source": [ 290 | "#The values at the beginning of the observation are taken as initial values\n", 291 | "inivals={}\n", 292 | "for n,var in enumerate([\"S\",\"I\",\"R\"]):\n", 293 | " inivals[var] = training_data[0,n]\n", 294 | "\n", 295 | "#The fraction of the initial exposed population is assumed twice the infected fraction\n", 296 | "inivals[\"E\"] = inivals[\"I\"] * 2.\n", 297 | "print(inivals)" 298 | ] 299 | }, 300 | { 301 | "cell_type": "code", 302 | "execution_count": 13, 303 | "metadata": {}, 304 | "outputs": [], 305 | "source": [ 306 | "inicoeffs={\"beta\": 0.50, \"gamma\": 0.20, \"sigma\": 0.20}" 307 | ] 308 | }, 309 | { 310 | "cell_type": "markdown", 311 | "metadata": {}, 312 | "source": [ 313 | "The function is initialized using the initial values, initial coefficients and functions (right-hand-sides of ODEs) provided above. Also specified are the output variables given in the training data, in the order in which they are present:" 314 | ] 315 | }, 316 | { 317 | "cell_type": "code", 318 | "execution_count": 22, 319 | "metadata": {}, 320 | "outputs": [ 321 | { 322 | "name": "stderr", 323 | "output_type": "stream", 324 | "text": [ 325 | "/Users/marty/Documents/GitHub/torchTS/examples/ode/SEIR/../../../torchts/nn/models/ode.py:39: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", 326 | " name: torch.tensor(value, device=self.device)\n" 327 | ] 328 | } 329 | ], 330 | "source": [ 331 | "# myopt=torch.optim.SGD(seir.coeffs.values(), 0.005)\n", 332 | "seir=ode.ODESolver(\n", 333 | " cfuncs, \n", 334 | " inivals, \n", 335 | " inicoeffs, \n", 336 | " dt=1, \n", 337 | " outvar=[\"S\",\"I\",\"R\"], \n", 338 | " optimizer=torch.optim.SGD,\n", 339 | " optimizer_args={\"lr\":0.005}\n", 340 | " )" 341 | ] 342 | }, 343 | { 344 | "cell_type": "code", 345 | "execution_count": 23, 346 | "metadata": {}, 347 | "outputs": [ 348 | { 349 | "name": "stderr", 350 | "output_type": "stream", 351 | "text": [ 352 | "GPU available: False, used: False\n", 353 | "TPU available: False, using: 0 TPU cores\n", 354 | "IPU available: False, using: 0 IPUs\n", 355 | "/opt/anaconda3/envs/torchTS/lib/python3.9/site-packages/pytorch_lightning/trainer/configuration_validator.py:122: UserWarning: You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\n", 356 | " rank_zero_warn(\"You defined a `validation_step` but have no `val_dataloader`. Skipping val loop.\")\n", 357 | "\n", 358 | " | Name | Type | Params\n", 359 | "------------------------------\n", 360 | "------------------------------\n", 361 | "3 Trainable params\n", 362 | "0 Non-trainable params\n", 363 | "3 Total params\n", 364 | "0.000 Total estimated model params size (MB)\n", 365 | "/opt/anaconda3/envs/torchTS/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:116: UserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", 366 | " rank_zero_warn(\n", 367 | "/opt/anaconda3/envs/torchTS/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:412: UserWarning: The number of training samples (1) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n", 368 | " rank_zero_warn(\n" 369 | ] 370 | }, 371 | { 372 | "name": "stdout", 373 | "output_type": "stream", 374 | "text": [ 375 | "Epoch 999: 100%|██████████| 1/1 [00:00<00:00, 10.96it/s, loss=0.00016, v_num=18, train_loss_step=0.000148, train_loss_epoch=0.000148] \n" 376 | ] 377 | } 378 | ], 379 | "source": [ 380 | "seir.fit(training_data, training_data, max_epochs=1000)" 381 | ] 382 | }, 383 | { 384 | "cell_type": "markdown", 385 | "metadata": {}, 386 | "source": [ 387 | "The values of the optimized coefficients can be retrieved like this:" 388 | ] 389 | }, 390 | { 391 | "cell_type": "code", 392 | "execution_count": 24, 393 | "metadata": {}, 394 | "outputs": [ 395 | { 396 | "data": { 397 | "text/plain": [ 398 | "{'beta': 0.40758606791496277,\n", 399 | " 'gamma': 0.3061103820800781,\n", 400 | " 'sigma': 0.016801264137029648}" 401 | ] 402 | }, 403 | "execution_count": 24, 404 | "metadata": {}, 405 | "output_type": "execute_result" 406 | } 407 | ], 408 | "source": [ 409 | "seir.get_coeffs()" 410 | ] 411 | }, 412 | { 413 | "cell_type": "markdown", 414 | "metadata": {}, 415 | "source": [ 416 | "Training using custom optimizer:" 417 | ] 418 | }, 419 | { 420 | "cell_type": "code", 421 | "execution_count": 25, 422 | "metadata": {}, 423 | "outputs": [], 424 | "source": [ 425 | "#myopt=torch.optim.SGD(seir.coeffs.values(), 0.005)\n", 426 | "#seir.fit(training_data, optimizer=myopt)" 427 | ] 428 | }, 429 | { 430 | "cell_type": "code", 431 | "execution_count": 26, 432 | "metadata": {}, 433 | "outputs": [], 434 | "source": [ 435 | "y_predict=seir.predict(nt)" 436 | ] 437 | }, 438 | { 439 | "cell_type": "code", 440 | "execution_count": 27, 441 | "metadata": {}, 442 | "outputs": [], 443 | "source": [ 444 | "import matplotlib.pyplot as plt" 445 | ] 446 | }, 447 | { 448 | "cell_type": "code", 449 | "execution_count": 28, 450 | "metadata": {}, 451 | "outputs": [ 452 | { 453 | "data": { 454 | "image/png": "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", 455 | "text/plain": [ 456 | "
" 457 | ] 458 | }, 459 | "metadata": {}, 460 | "output_type": "display_data" 461 | } 462 | ], 463 | "source": [ 464 | "fig,ax=plt.subplots()\n", 465 | "clr=[\"b\",\"r\",\"g\"]\n", 466 | "\n", 467 | "for n,var in enumerate(seir.outvar):\n", 468 | " ax.plot(test_data[:nt,n], label=\"%s (obs)\" % var, color=clr[n])\n", 469 | " ax.plot(y_predict.detach()[:,n], label=\"%s (sim)\" % var, \n", 470 | " linestyle=\"dashed\", color=clr[n])\n", 471 | " \n", 472 | " ax.set_ylim(0, 1.00)\n", 473 | " ax.vlines(nt_train, 0, 1.00, color='k', linestyle='dotted')\n", 474 | " ax.legend(loc='center left')\n", 475 | " ax.set_xlabel(\"Time (days)\")\n", 476 | " ax.set_ylabel(\"Population Fraction\")\n", 477 | "\n", 478 | "plt.show()" 479 | ] 480 | }, 481 | { 482 | "cell_type": "markdown", 483 | "metadata": {}, 484 | "source": [ 485 | "The Figure above shows the observed (solid) against the simulated (dashed) compartment fractions. The vertical, dotted line marks the end of the training window; values beyond 30 days are predicted from the minimum misfit model." 486 | ] 487 | }, 488 | { 489 | "cell_type": "code", 490 | "execution_count": null, 491 | "metadata": {}, 492 | "outputs": [], 493 | "source": [] 494 | } 495 | ], 496 | "metadata": { 497 | "kernelspec": { 498 | "display_name": "Python 3", 499 | "language": "python", 500 | "name": "python3" 501 | }, 502 | "language_info": { 503 | "codemirror_mode": { 504 | "name": "ipython", 505 | "version": 3 506 | }, 507 | "file_extension": ".py", 508 | "mimetype": "text/x-python", 509 | "name": "python", 510 | "nbconvert_exporter": "python", 511 | "pygments_lexer": "ipython3", 512 | "version": "3.9.6" 513 | } 514 | }, 515 | "nbformat": 4, 516 | "nbformat_minor": 4 517 | } 518 | -------------------------------------------------------------------------------- /examples/ode/SEIR/SIR_data_SD_county.pt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/examples/ode/SEIR/SIR_data_SD_county.pt -------------------------------------------------------------------------------- /examples/quantile-regression/README.md: -------------------------------------------------------------------------------- 1 | # LSTM with Quantile Regression 2 | 3 | This notebook provides an example of using a quantile loss function with a LSTM network. 4 | 5 | Assuming TorchTS is already installed, the additional dependencies can be installed with `pip`: 6 | 7 | ```bash 8 | pip install -r requirements.txt 9 | ``` 10 | -------------------------------------------------------------------------------- /examples/quantile-regression/requirements.txt: -------------------------------------------------------------------------------- 1 | matplotlib 2 | ipykernel 3 | -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [tool.poetry] 2 | name = "torchts" 3 | version = "0.1.1" 4 | description = "Time series forecasting with PyTorch" 5 | authors = [ 6 | "TorchTS Team " 7 | ] 8 | license = "MIT" 9 | readme = "README.md" 10 | homepage = "https://rose-stl-lab.github.io/torchTS" 11 | repository = "https://github.com/Rose-STL-Lab/torchTS" 12 | documentation = "https://rose-stl-lab.github.io/torchTS/docs" 13 | keywords = ["deep-learning", "machine-learning", "time-series", "pytorch"] 14 | classifiers = [ 15 | "Development Status :: 3 - Alpha", 16 | "Intended Audience :: Developers", 17 | "Intended Audience :: Science/Research", 18 | "Topic :: Scientific/Engineering", 19 | "Topic :: Software Development :: Libraries :: Python Modules" 20 | ] 21 | 22 | [tool.poetry.dependencies] 23 | python = ">=3.8,<3.10" 24 | torch = "^1.4" 25 | pytorch-lightning = "^1.2" 26 | scipy = "^1.7.1" 27 | 28 | [tool.poetry.group.test.dependencies] 29 | pytest = "^7.0.1" 30 | pytest-cov = "^3.0.0" 31 | pytest-mock = "^3.7.0" 32 | pre-commit = "^2.17.0" 33 | 34 | [tool.poetry.group.docs.dependencies] 35 | sphinx = "^4.4.0" 36 | asteroid-sphinx-theme = "^0.0.3" 37 | 38 | [build-system] 39 | requires = ["poetry-core>=1.0.0"] 40 | build-backend = "poetry.core.masonry.api" 41 | 42 | [tool.black] 43 | line-length = 88 44 | 45 | [tool.isort] 46 | profile = "black" 47 | 48 | [tool.flakeheaven] 49 | exclude = ["*.ipynb"] 50 | format = "grouped" 51 | max_complexity = 10 52 | max_line_length = 88 53 | show_source = true 54 | 55 | [tool.flakeheaven.plugins] 56 | pycodestyle = ["+*", "-E203", "-E741"] 57 | pyflakes = ["+*"] 58 | mccabe = ["+*"] 59 | flake8-bugbear = ["+*"] 60 | flake8-comprehensions = ["+*"] 61 | -------------------------------------------------------------------------------- /scripts/build_docs.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # run this script from the project root using `./scripts/build_docs.sh` 3 | 4 | echo "----------------------------------------" 5 | echo "Generating API documentation with Sphinx" 6 | echo "----------------------------------------" 7 | 8 | poetry run make -C docs html 9 | 10 | echo "-----------------------------------------" 11 | echo "Moving Sphinx documentation to Docusaurus" 12 | echo "-----------------------------------------" 13 | 14 | SPHINX_HTML_DIR="website/static/api/" 15 | cp -R "./docs/build/html/" "./${SPHINX_HTML_DIR}" 16 | echo "Sucessfully moved Sphinx docs to ${SPHINX_HTML_DIR}" 17 | -------------------------------------------------------------------------------- /tests/nn/test_loss.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import torch 3 | 4 | from torchts.nn.loss import masked_mae_loss, mis_loss, quantile_loss 5 | 6 | 7 | @pytest.fixture 8 | def y_true(): 9 | data = [1, 2, 3] 10 | return torch.tensor(data) 11 | 12 | 13 | @pytest.fixture 14 | def y_pred(): 15 | data = [1.1, 1.9, 3.1] 16 | return torch.tensor(data) 17 | 18 | 19 | def test_masked_mae_loss(y_true, y_pred): 20 | """Test masked_mae_loss()""" 21 | loss = masked_mae_loss(y_pred, y_true) 22 | assert loss == pytest.approx(0.1) 23 | 24 | 25 | @pytest.mark.parametrize( 26 | "lower, upper, interval, expected_loss", 27 | [ 28 | ([1, 2, 3], [1.1, 2.1, 3.1], 0.8, 0.1), 29 | ([0.9, 1.9, 2.9], [1.1, 2.1, 3.1], 0.8, 0.2), 30 | ([0.9, 1.9, 2.9], [1.1, 2.1, 3.1], 0.95, 0.2), 31 | ([0.7, 1.9, 2.9], [0.9, 2.1, 3.1], 0.8, 1.6 / 3), 32 | ([0.7, 1.9, 2.9], [0.9, 2.1, 3.1], 0.95, 4.6 / 3), 33 | ([0.9, 1.9, 3.1], [1.1, 2.1, 3.3], 0.8, 1.6 / 3), 34 | ([0.9, 1.9, 3.1], [1.1, 2.1, 3.3], 0.95, 4.6 / 3), 35 | ], 36 | ) 37 | def test_mis_loss(y_true, lower, upper, interval, expected_loss): 38 | """Test quantile_loss()""" 39 | y_true = y_true.reshape(-1, 1) 40 | y_pred = torch.transpose(torch.tensor([lower, upper]), 0, 1) 41 | loss = mis_loss(y_pred, y_true, interval) 42 | assert loss == pytest.approx(expected_loss) 43 | 44 | 45 | @pytest.mark.parametrize( 46 | "quantile, expected_loss", [(0.05, 0.065), (0.5, 0.05), (0.95, 0.035)] 47 | ) 48 | def test_quantile_loss(y_true, y_pred, quantile, expected_loss): 49 | """Test quantile_loss()""" 50 | loss = quantile_loss(y_pred, y_true, quantile) 51 | assert loss == pytest.approx(expected_loss) 52 | -------------------------------------------------------------------------------- /tests/nn/test_ode.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pytest 3 | import torch 4 | 5 | from torchts.nn.models.ode import ODESolver 6 | from torchts.utils.data import generate_ode_dataset 7 | 8 | 9 | @pytest.fixture 10 | def euler_model(): 11 | # ODE: x'(t) = 2x 12 | model = ODESolver( 13 | {"x": lambda prev_val, coeffs: coeffs["alpha"] * prev_val["x"]}, 14 | {"x": 1.0}, 15 | {"alpha": 2.0}, 16 | 0.1, 17 | solver="euler", 18 | optimizer=torch.optim.Adam, 19 | optimizer_args={"lr": 0.5}, 20 | ) 21 | preds = model(2) 22 | return model, preds 23 | 24 | 25 | @pytest.fixture 26 | def rk4_model(): 27 | # ODE: x'(t) = x 28 | model = ODESolver( 29 | {"x": lambda prev_val, coeffs: prev_val["x"]}, 30 | {"x": 1.0}, 31 | {}, 32 | 0.1, 33 | solver="rk4", 34 | optimizer=None, 35 | ) 36 | preds = model(2) 37 | return model, preds 38 | 39 | 40 | def test_euler(euler_model): 41 | """Test Euler's Method""" 42 | model, preds = euler_model 43 | assert model.step_solver == model._euler_step 44 | assert model.get_coeffs() == {"alpha": 2.0} 45 | assert preds[1, 0].item() == pytest.approx(1.2, abs=1e-6) 46 | 47 | 48 | def test_rk4(rk4_model): 49 | """Test 4th order Runge-Kutta Method""" 50 | model, preds = rk4_model 51 | assert model.step_solver == model._runge_kutta_4_step 52 | assert preds[1, 0].item() == pytest.approx(np.exp(0.1), abs=1e-6) 53 | 54 | 55 | def test_generate_ode_dataset(euler_model): 56 | """Test the generate_ode_dataset function""" 57 | model, preds = euler_model 58 | x1, x2 = generate_ode_dataset(preds) 59 | assert x1 == preds[:-1, :] 60 | assert x2 == preds[1:, :] 61 | 62 | 63 | def test_value_errors(): 64 | """Detects ValueError in the __init__ function""" 65 | with pytest.raises(ValueError, match="Unrecognized solver .*"): 66 | _ = ODESolver( 67 | {"x": lambda prev_val, coeffs: coeffs["alpha"] * prev_val["x"]}, 68 | {"x": 1.0}, 69 | {"alpha": 2.0}, 70 | 0.1, 71 | solver="a", 72 | optimizer=None, 73 | ) 74 | 75 | with pytest.raises(ValueError, match="Inconsistent keys in ode and init_vars"): 76 | _ = ODESolver( 77 | {"x": lambda prev_val, coeffs: coeffs["alpha"] * prev_val["x"]}, 78 | {"x": 1.0, "y": 2.0}, 79 | {"alpha": 2.0}, 80 | 0.1, 81 | solver="euler", 82 | optimizer=None, 83 | ) 84 | 85 | 86 | def test_step_backward(euler_model): 87 | """Test the step and backward function""" 88 | torch.manual_seed(0) 89 | batch = torch.Tensor([[1.0]]), torch.Tensor([[1.1]]) 90 | model, _ = euler_model 91 | loss = model._step(batch, 0, 0) 92 | assert (loss.item() - (1.2 - 1.1) ** 2) < 1e-6 93 | model.backward(loss, None, 0) 94 | model.optimizer(model.parameters()).step() 95 | coeffs = model.get_coeffs() 96 | assert coeffs["alpha"] < 2 97 | 98 | 99 | def test_fit(euler_model): 100 | """Test the step and backward function""" 101 | torch.manual_seed(0) 102 | model, _ = euler_model 103 | model.fit(torch.Tensor([[1.0]]), torch.Tensor([[1.1]]), max_epochs=1, batch_size=1) 104 | coeffs = model.get_coeffs() 105 | assert coeffs["alpha"] < 2 106 | -------------------------------------------------------------------------------- /tests/test_model.py: -------------------------------------------------------------------------------- 1 | from functools import partial 2 | 3 | import pytest 4 | import torch 5 | from torch import nn, optim 6 | from torch.utils.data import DataLoader, TensorDataset 7 | 8 | from torchts.nn.model import TimeSeriesModel 9 | 10 | 11 | class LinearModel(TimeSeriesModel): 12 | def __init__(self, slope, intercept, **kwargs): 13 | super().__init__(**kwargs) 14 | self.line = nn.Linear(1, 1) 15 | self.line.weight = nn.Parameter(slope * torch.ones_like(self.line.weight)) 16 | self.line.bias = nn.Parameter(intercept * torch.ones_like(self.line.bias)) 17 | 18 | def forward(self, x, y=None, batches_seen=None): 19 | return self.line(x) 20 | 21 | 22 | def test_forward(): 23 | slope = 2 24 | intercept = -1 25 | model = LinearModel( 26 | slope, intercept, optimizer=optim.SGD, optimizer_args={"lr": 0.01} 27 | ) 28 | 29 | x = torch.Tensor([-1, 0, 1]).reshape(-1, 1) 30 | y = slope * x + intercept 31 | 32 | assert (model(x) == y).all() 33 | assert (model.predict(x) == y).all() 34 | 35 | 36 | def test_step(mocker): 37 | slope = 2 38 | intercept = -1 39 | quantile = 0.5 40 | quantile_loss = mocker.MagicMock() 41 | model = LinearModel( 42 | slope, 43 | intercept, 44 | optimizer=optim.SGD, 45 | criterion=quantile_loss, 46 | criterion_args={"quantile": quantile}, 47 | ) 48 | 49 | x = torch.Tensor([0]).reshape(-1, 1) 50 | y = slope * x + intercept 51 | dataset = TensorDataset(x, y) 52 | loader = DataLoader(dataset, batch_size=1, shuffle=False) 53 | 54 | for i, batch in enumerate(loader): 55 | model._step(batch, i, len(loader)) 56 | x, y = batch 57 | quantile_loss.assert_called_once_with(y, y, quantile=quantile) 58 | 59 | 60 | def test_train(): 61 | torch.manual_seed(0) 62 | 63 | slope_init = 2 64 | intercept_init = -1 65 | optimizer = partial(optim.SGD, lr=0.1) 66 | model = LinearModel(slope_init, intercept_init, optimizer=optimizer) 67 | 68 | slope_true = 1 69 | intercept_true = 0 70 | n = 1000 71 | x = torch.rand(n, 1) 72 | y = slope_true * x + intercept_true 73 | 74 | max_epochs = 100 75 | model.fit(x, y, max_epochs=max_epochs) 76 | 77 | tol = 1e-4 78 | assert pytest.approx(model.line.weight.detach(), abs=tol) == slope_true 79 | assert pytest.approx(model.line.bias.detach(), abs=tol) == intercept_true 80 | -------------------------------------------------------------------------------- /tests/test_sliding_window.py: -------------------------------------------------------------------------------- 1 | import pytest 2 | import torch 3 | 4 | from torchts.utils.data import sliding_window 5 | 6 | 7 | @pytest.fixture 8 | def tensor(): 9 | n = 10 10 | return torch.IntTensor(range(n)) 11 | 12 | 13 | @pytest.mark.parametrize("lag", [2, 5, [1, 2, 3], {1, 2, 3}, [1, 3, 5]]) 14 | @pytest.mark.parametrize("horizon", [1, 2]) 15 | def test_shape(tensor, lag, horizon): 16 | x, y = sliding_window(tensor, lag, horizon=horizon) 17 | 18 | if isinstance(lag, int): 19 | rows = len(tensor) - lag - horizon + 1 20 | cols = lag 21 | else: 22 | rows = len(tensor) - max(lag) - horizon + 1 23 | cols = len(lag) 24 | 25 | assert len(x.shape) == 2 26 | assert x.shape[0] == rows 27 | assert x.shape[1] == cols 28 | 29 | assert len(y.shape) == 1 30 | assert y.shape[0] == rows 31 | 32 | 33 | @pytest.mark.parametrize("lag", [2, 5, [1, 2, 3], {1, 2, 3}, [1, 3, 5]]) 34 | @pytest.mark.parametrize("horizon", [1, 2]) 35 | def test_value(tensor, lag, horizon): 36 | x, y = sliding_window(tensor, lag, horizon=horizon) 37 | 38 | if isinstance(lag, int): 39 | for i in range(x.shape[0]): 40 | j = lag + i 41 | assert (x[i, :] == tensor[i:j]).all() 42 | else: 43 | for i in range(x.shape[0]): 44 | assert (x[i, :] == tensor[[x - 1 + i for x in lag]]).all() 45 | 46 | assert (y - x[:, -1] == horizon).all() 47 | 48 | 49 | @pytest.mark.parametrize("lag", ["1", 1.0, ["1"], [1, "2", 3], {1, 2.0, 3}]) 50 | def test_non_int(tensor, lag): 51 | with pytest.raises(TypeError): 52 | sliding_window(tensor, lag) 53 | 54 | 55 | @pytest.mark.parametrize("lag", [-1, 0, [0, 1, 2], {0, 1, 2}, [-1, 1, 2]]) 56 | def test_non_positive(tensor, lag): 57 | with pytest.raises(ValueError): 58 | sliding_window(tensor, lag) 59 | -------------------------------------------------------------------------------- /torchts/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/torchts/__init__.py -------------------------------------------------------------------------------- /torchts/nn/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/torchts/nn/__init__.py -------------------------------------------------------------------------------- /torchts/nn/graph.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | 4 | from torchts.utils import graph 5 | from torchts.utils.data import concat 6 | 7 | 8 | class DCGRUCell(nn.Module): 9 | def __init__( 10 | self, 11 | num_units, 12 | adj_mx, 13 | max_diffusion_step, 14 | num_nodes, 15 | input_dim, 16 | activation=torch.tanh, 17 | filter_type="laplacian", 18 | use_gc_for_ru=True, 19 | ): 20 | super().__init__() 21 | self._activation = activation 22 | self._num_nodes = num_nodes 23 | self._num_units = num_units 24 | self._max_diffusion_step = max_diffusion_step 25 | self._use_gc_for_ru = use_gc_for_ru 26 | 27 | supports = [] 28 | 29 | if filter_type == "laplacian": 30 | supports.append(graph.scaled_laplacian(adj_mx, lambda_max=None)) 31 | elif filter_type == "random_walk": 32 | supports.append(graph.random_walk(adj_mx).T) 33 | elif filter_type == "dual_random_walk": 34 | supports.append(graph.random_walk(adj_mx).T) 35 | supports.append(graph.reverse_random_walk(adj_mx).T) 36 | else: 37 | supports.append(graph.scaled_laplacian(adj_mx)) 38 | 39 | supports = [graph.sparse_matrix(s) for s in supports] 40 | supports = torch.cat([s.unsqueeze(dim=0) for s in supports]) 41 | self.register_buffer("_supports", supports) 42 | 43 | num_matrices = len(supports) * self._max_diffusion_step + 1 44 | input_size_fc = self._num_units + input_dim 45 | input_size_gconv = input_size_fc * num_matrices 46 | input_size_ru = input_size_gconv if self._use_gc_for_ru else input_size_fc 47 | 48 | output_size = 2 * self._num_units 49 | self._ru_weights = nn.Parameter(torch.empty(input_size_ru, output_size)) 50 | self._ru_biases = nn.Parameter(torch.empty(output_size)) 51 | nn.init.xavier_normal_(self._ru_weights, gain=1.0) 52 | nn.init.constant_(self._ru_biases, val=1.0) 53 | 54 | output_size = self._num_units 55 | self._gconv_weights = nn.Parameter(torch.empty(input_size_gconv, output_size)) 56 | self._gconv_biases = nn.Parameter(torch.empty(output_size)) 57 | nn.init.xavier_normal_(self._gconv_weights, gain=1.0) 58 | nn.init.constant_(self._gconv_biases, val=0.0) 59 | 60 | def forward(self, inputs, hx): 61 | fn = self._gconv if self._use_gc_for_ru else self._fc 62 | output_size = 2 * self._num_units 63 | value = torch.sigmoid(fn(inputs, hx, output_size, bias_start=1.0, reset=True)) 64 | value = torch.reshape(value, (-1, self._num_nodes, output_size)) 65 | r, u = torch.split(tensor=value, split_size_or_sections=self._num_units, dim=-1) 66 | r = torch.reshape(r, (-1, self._num_nodes * self._num_units)) 67 | u = torch.reshape(u, (-1, self._num_nodes * self._num_units)) 68 | c = self._gconv(inputs, r * hx, self._num_units) 69 | 70 | if self._activation is not None: 71 | c = self._activation(c) 72 | 73 | return u * hx + (1.0 - u) * c 74 | 75 | def _fc(self, inputs, state, output_size, bias_start=0.0, reset=True): 76 | batch_size = inputs.shape[0] 77 | shape = (batch_size * self._num_nodes, -1) 78 | inputs = torch.reshape(inputs, shape) 79 | state = torch.reshape(state, shape) 80 | x = torch.cat([inputs, state], dim=-1) 81 | 82 | return torch.matmul(x, self._ru_weights) + self._ru_biases 83 | 84 | def _gconv(self, inputs, state, output_size, bias_start=0.0, reset=False): 85 | batch_size = inputs.shape[0] 86 | shape = (batch_size, self._num_nodes, -1) 87 | inputs = torch.reshape(inputs, shape) 88 | state = torch.reshape(state, shape) 89 | x = torch.cat([inputs, state], dim=2) 90 | input_size = x.size(2) 91 | 92 | x0 = x.permute(1, 2, 0) 93 | x0 = torch.reshape(x0, shape=[self._num_nodes, input_size * batch_size]) 94 | x = torch.unsqueeze(x0, 0) 95 | 96 | if self._max_diffusion_step > 0: 97 | for support in self._supports: 98 | x1 = torch.sparse.mm(support, x0) 99 | x = concat(x, x1) 100 | 101 | for _ in range(2, self._max_diffusion_step + 1): 102 | x2 = 2 * torch.sparse.mm(support, x1) - x0 103 | x = concat(x, x2) 104 | x1, x0 = x2, x1 105 | 106 | num_matrices = len(self._supports) * self._max_diffusion_step + 1 107 | x = torch.reshape( 108 | x, shape=[num_matrices, self._num_nodes, input_size, batch_size] 109 | ) 110 | x = x.permute(3, 1, 2, 0) 111 | x = torch.reshape( 112 | x, shape=[batch_size * self._num_nodes, input_size * num_matrices] 113 | ) 114 | 115 | if reset: 116 | weights, biases = self._ru_weights, self._ru_biases 117 | else: 118 | weights, biases = self._gconv_weights, self._gconv_biases 119 | 120 | x = torch.matmul(x, weights) + biases 121 | 122 | return torch.reshape(x, [batch_size, self._num_nodes * output_size]) 123 | 124 | 125 | class DCGRU(nn.Module): 126 | def __init__( 127 | self, 128 | num_layers, 129 | num_units, 130 | adj_mx, 131 | max_diffusion_step, 132 | num_nodes, 133 | input_dim, 134 | activation=torch.tanh, 135 | filter_type="laplacian", 136 | use_gc_for_ru=True, 137 | ): 138 | super().__init__() 139 | self.layers = nn.ModuleList( 140 | DCGRUCell( 141 | num_units, 142 | adj_mx, 143 | max_diffusion_step, 144 | num_nodes, 145 | input_dim if i == 0 else num_units, 146 | filter_type=filter_type, 147 | use_gc_for_ru=use_gc_for_ru, 148 | ) 149 | for i in range(num_layers) 150 | ) 151 | 152 | def forward(self, inputs, hidden_state): 153 | hidden_states = [] 154 | output = inputs 155 | 156 | for i, layer in enumerate(self.layers): 157 | output = layer(output, hidden_state[i]) 158 | hidden_states.append(output) 159 | 160 | return output, torch.stack(hidden_states) 161 | -------------------------------------------------------------------------------- /torchts/nn/loss.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def masked_mae_loss(y_pred, y_true): 5 | """Calculate masked mean absolute error loss 6 | 7 | Args: 8 | y_pred (torch.Tensor): Predicted values 9 | y_true (torch.Tensor): True values 10 | 11 | Returns: 12 | torch.Tensor: Loss 13 | """ 14 | mask = (y_true != 0).float() 15 | mask /= mask.mean() 16 | 17 | loss = torch.abs(y_pred - y_true) 18 | loss = loss * mask 19 | loss[torch.isnan(loss)] = 0 20 | 21 | return loss.mean() 22 | 23 | 24 | def mis_loss( 25 | y_pred: torch.tensor, y_true: torch.tensor, interval: float 26 | ) -> torch.tensor: 27 | """Calculate MIS loss 28 | 29 | Args: 30 | y_pred (torch.tensor): Predicted values 31 | y_true (torch.tensor): True values 32 | interval (float): confidence interval (e.g. 0.95 for 95% confidence interval) 33 | 34 | Returns: 35 | torch.tensor: output losses 36 | """ 37 | alpha = 1 - interval 38 | lower = y_pred[:, 0::2] 39 | upper = y_pred[:, 1::2] 40 | 41 | loss = upper - lower 42 | loss = torch.max(loss, loss + (2 / alpha) * (lower - y_true)) 43 | loss = torch.max(loss, loss + (2 / alpha) * (y_true - upper)) 44 | loss = torch.mean(loss) 45 | 46 | return loss 47 | 48 | 49 | def quantile_loss(y_pred: torch.tensor, y_true: torch.tensor, quantile: float) -> float: 50 | """Calculate quantile loss 51 | 52 | Args: 53 | y_pred (torch.tensor): Predicted values 54 | y_true (torch.tensor): True values 55 | quantile (float): quantile (e.g. 0.5 for median) 56 | 57 | Returns: 58 | float: output losses 59 | """ 60 | errors = y_true - y_pred 61 | loss = torch.max((quantile - 1) * errors, quantile * errors) 62 | loss = torch.mean(loss) 63 | return loss 64 | -------------------------------------------------------------------------------- /torchts/nn/model.py: -------------------------------------------------------------------------------- 1 | from abc import abstractmethod 2 | from functools import partial 3 | 4 | import torch.nn.functional as F 5 | from pytorch_lightning import LightningModule, Trainer 6 | from torch.utils.data import DataLoader, TensorDataset 7 | 8 | 9 | class TimeSeriesModel(LightningModule): 10 | """Base class for all TorchTS models. 11 | 12 | Args: 13 | optimizer (torch.optim.Optimizer): Optimizer 14 | opimizer_args (dict): Arguments for the optimizer 15 | criterion: Loss function 16 | criterion_args (dict): Arguments for the loss function 17 | scheduler (torch.optim.lr_scheduler): Learning rate scheduler 18 | scheduler_args (dict): Arguments for the scheduler 19 | scaler (torchts.utils.scaler.Scaler): Scaler 20 | """ 21 | 22 | def __init__( 23 | self, 24 | optimizer, 25 | optimizer_args=None, 26 | criterion=F.mse_loss, 27 | criterion_args=None, 28 | scheduler=None, 29 | scheduler_args=None, 30 | scaler=None, 31 | ): 32 | super().__init__() 33 | self.criterion = criterion 34 | self.criterion_args = criterion_args 35 | self.scaler = scaler 36 | 37 | if optimizer_args is not None: 38 | self.optimizer = partial(optimizer, **optimizer_args) 39 | else: 40 | self.optimizer = optimizer 41 | 42 | if scheduler is not None and scheduler_args is not None: 43 | self.scheduler = partial(scheduler, **scheduler_args) 44 | else: 45 | self.scheduler = scheduler 46 | 47 | def fit(self, x, y, max_epochs=10, batch_size=128): 48 | """Fits model to the given data. 49 | 50 | Args: 51 | x (torch.Tensor): Input data 52 | y (torch.Tensor): Output data 53 | max_epochs (int): Number of training epochs 54 | batch_size (int): Batch size for torch.utils.data.DataLoader 55 | """ 56 | dataset = TensorDataset(x, y) 57 | loader = DataLoader(dataset, batch_size=batch_size, shuffle=True) 58 | trainer = Trainer(max_epochs=max_epochs) 59 | trainer.fit(self, loader) 60 | 61 | def prepare_batch(self, batch): 62 | return batch 63 | 64 | def _step(self, batch, batch_idx, num_batches): 65 | """ 66 | 67 | Args: 68 | batch: Output of the torch.utils.data.DataLoader 69 | batch_idx: Integer displaying index of this batch 70 | dataset: Data set to use 71 | 72 | Returns: loss for the batch 73 | """ 74 | x, y = self.prepare_batch(batch) 75 | 76 | if self.training: 77 | batches_seen = batch_idx + self.current_epoch * num_batches 78 | else: 79 | batches_seen = batch_idx 80 | 81 | pred = self(x, y, batches_seen) 82 | 83 | if self.scaler is not None: 84 | y = self.scaler.inverse_transform(y) 85 | pred = self.scaler.inverse_transform(pred) 86 | 87 | if self.criterion_args is not None: 88 | loss = self.criterion(pred, y, **self.criterion_args) 89 | else: 90 | loss = self.criterion(pred, y) 91 | 92 | return loss 93 | 94 | def training_step(self, batch, batch_idx): 95 | """Trains model for one step. 96 | 97 | Args: 98 | batch (torch.Tensor): Output of the torch.utils.data.DataLoader 99 | batch_idx (int): Integer displaying index of this batch 100 | """ 101 | train_loss = self._step(batch, batch_idx, len(self.trainer.train_dataloader)) 102 | self.log( 103 | "train_loss", 104 | train_loss, 105 | on_step=True, 106 | on_epoch=True, 107 | prog_bar=True, 108 | logger=True, 109 | ) 110 | return train_loss 111 | 112 | def validation_step(self, batch, batch_idx): 113 | """Validates model for one step. 114 | 115 | Args: 116 | batch (torch.Tensor): Output of the torch.utils.data.DataLoader 117 | batch_idx (int): Integer displaying index of this batch 118 | """ 119 | val_loss = self._step(batch, batch_idx, len(self.trainer.val_dataloader)) 120 | self.log("val_loss", val_loss) 121 | return val_loss 122 | 123 | def test_step(self, batch, batch_idx): 124 | """Tests model for one step. 125 | 126 | Args: 127 | batch (torch.Tensor): Output of the torch.utils.data.DataLoader 128 | batch_idx (int): Integer displaying index of this batch 129 | """ 130 | test_loss = self._step(batch, batch_idx, len(self.trainer.test_dataloader)) 131 | self.log("test_loss", test_loss) 132 | return test_loss 133 | 134 | @abstractmethod 135 | def forward(self, x, y=None, batches_seen=None): 136 | """Forward pass. 137 | 138 | Args: 139 | x (torch.Tensor): Input data 140 | 141 | Returns: 142 | torch.Tensor: Predicted data 143 | """ 144 | 145 | def predict(self, x): 146 | """Runs model inference. 147 | 148 | Args: 149 | x (torch.Tensor): Input data 150 | 151 | Returns: 152 | torch.Tensor: Predicted data 153 | """ 154 | return self(x).detach() 155 | 156 | def configure_optimizers(self): 157 | """Configure optimizer. 158 | 159 | Returns: 160 | torch.optim.Optimizer: Optimizer 161 | """ 162 | optimizer = self.optimizer(self.parameters()) 163 | 164 | if self.scheduler is not None: 165 | scheduler = self.scheduler(optimizer) 166 | return [optimizer], [scheduler] 167 | 168 | return optimizer 169 | -------------------------------------------------------------------------------- /torchts/nn/models/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/torchts/nn/models/__init__.py -------------------------------------------------------------------------------- /torchts/nn/models/dcrnn.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | from torch import nn 4 | 5 | from torchts.nn.graph import DCGRU 6 | from torchts.nn.model import TimeSeriesModel 7 | 8 | 9 | class Encoder(nn.Module): 10 | def __init__(self, input_dim, seq_len, **kwargs): 11 | super().__init__() 12 | self.input_dim = input_dim 13 | self.seq_len = seq_len 14 | self.dcgru = DCGRU(input_dim=self.input_dim, **kwargs) 15 | 16 | def forward(self, inputs, hidden_state): 17 | output, hidden = self.dcgru(inputs, hidden_state) 18 | return output, hidden 19 | 20 | 21 | class Decoder(nn.Module): 22 | def __init__(self, output_dim, horizon, **kwargs): 23 | super().__init__() 24 | self.output_dim = output_dim 25 | self.horizon = horizon 26 | self.num_nodes = kwargs["num_nodes"] 27 | self.num_units = kwargs["num_units"] 28 | self.dcgru = DCGRU(input_dim=self.output_dim, **kwargs) 29 | self.projection_layer = nn.Linear(self.num_units, self.output_dim) 30 | 31 | def forward(self, inputs, hidden_state): 32 | output, hidden = self.dcgru(inputs, hidden_state) 33 | projected = self.projection_layer(output.view(-1, self.num_units)) 34 | output = projected.view(-1, self.num_nodes * self.output_dim) 35 | return output, hidden 36 | 37 | 38 | class DCRNN(TimeSeriesModel): 39 | def __init__( 40 | self, 41 | adj_mx, 42 | num_units, 43 | seq_len=1, 44 | horizon=1, 45 | input_dim=1, 46 | output_dim=1, 47 | max_diffusion_step=2, 48 | filter_type="laplacian", 49 | num_nodes=1, 50 | num_layers=1, 51 | use_gc_for_ru=True, 52 | use_curriculum_learning=False, 53 | cl_decay_steps=1000, 54 | **kwargs, 55 | ): 56 | super().__init__(**kwargs) 57 | 58 | dcgru_args = { 59 | "adj_mx": adj_mx, 60 | "num_nodes": num_nodes, 61 | "num_layers": num_layers, 62 | "num_units": num_units, 63 | "max_diffusion_step": max_diffusion_step, 64 | "filter_type": filter_type, 65 | "use_gc_for_ru": use_gc_for_ru, 66 | } 67 | 68 | self.encoder_model = Encoder(input_dim, seq_len, **dcgru_args) 69 | self.decoder_model = Decoder(output_dim, horizon, **dcgru_args) 70 | 71 | self.num_nodes = num_nodes 72 | self.num_layers = num_layers 73 | self.hidden_state_size = num_nodes * num_units 74 | self.use_curriculum_learning = use_curriculum_learning 75 | self.cl_decay_steps = cl_decay_steps 76 | 77 | def _compute_sampling_threshold(self, batches_seen): 78 | return self.cl_decay_steps / ( 79 | self.cl_decay_steps + np.exp(batches_seen / self.cl_decay_steps) 80 | ) 81 | 82 | def encoder(self, inputs): 83 | batch_size = inputs.size(1) 84 | shape = self.num_layers, batch_size, self.hidden_state_size 85 | encoder_hidden_state = torch.zeros(shape, device=self.device) 86 | 87 | for t in range(self.encoder_model.seq_len): 88 | _, encoder_hidden_state = self.encoder_model( 89 | inputs[t], encoder_hidden_state 90 | ) 91 | 92 | return encoder_hidden_state 93 | 94 | def decoder(self, encoder_hidden_state, labels=None, batches_seen=None): 95 | batch_size = encoder_hidden_state.size(1) 96 | shape = batch_size, self.num_nodes * self.decoder_model.output_dim 97 | go_symbol = torch.zeros(shape, device=self.device) 98 | decoder_hidden_state = encoder_hidden_state 99 | decoder_input = go_symbol 100 | outputs = [] 101 | 102 | for t in range(self.decoder_model.horizon): 103 | decoder_output, decoder_hidden_state = self.decoder_model( 104 | decoder_input, decoder_hidden_state 105 | ) 106 | decoder_input = decoder_output 107 | outputs.append(decoder_output) 108 | 109 | if self.training and self.use_curriculum_learning: 110 | c = np.random.uniform(0, 1) 111 | 112 | if c < self._compute_sampling_threshold(batches_seen): 113 | decoder_input = labels[t] 114 | 115 | outputs = torch.stack(outputs) 116 | return outputs 117 | 118 | def forward(self, inputs, labels=None, batches_seen=None): 119 | encoder_hidden_state = self.encoder(inputs) 120 | outputs = self.decoder(encoder_hidden_state, labels, batches_seen=batches_seen) 121 | 122 | return outputs 123 | 124 | def prepare_batch(self, batch): 125 | x, y = batch 126 | x = x.permute(1, 0, 2, 3) 127 | y = y.permute(1, 0, 2, 3) 128 | 129 | batch_size = x.size(1) 130 | x = x.view( 131 | self.encoder_model.seq_len, 132 | batch_size, 133 | self.num_nodes * self.encoder_model.input_dim, 134 | ) 135 | y = y[..., : self.decoder_model.output_dim].view( 136 | self.decoder_model.horizon, 137 | batch_size, 138 | self.num_nodes * self.decoder_model.output_dim, 139 | ) 140 | 141 | return x, y 142 | -------------------------------------------------------------------------------- /torchts/nn/models/ode.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | 4 | from torchts.nn.model import TimeSeriesModel 5 | 6 | 7 | class ODESolver(TimeSeriesModel): 8 | def __init__( 9 | self, ode, init_vars, init_coeffs, dt, solver="euler", outvar=None, **kwargs 10 | ): 11 | """TimeSeriesModel for modeling ordinary differential equations. 12 | 13 | Args: 14 | ode (dict): ODE in dictionary form 15 | init_vars (dict): Initial values for each variable 16 | init_coeffs (dict): Initial values for each parameter 17 | dt (float): Time step 18 | solver (str): Numerical method for solving the ODE ("euler"/"rk4") 19 | outvar (list): Observed variables 20 | kwargs (Any): Additional arguments passed to TimeSeriesModel 21 | """ 22 | super().__init__(**kwargs) 23 | 24 | if ode.keys() != init_vars.keys(): 25 | raise ValueError("Inconsistent keys in ode and init_vars") 26 | 27 | if solver == "euler": 28 | self.step_solver = self._euler_step 29 | elif solver == "rk4": 30 | self.step_solver = self._runge_kutta_4_step 31 | else: 32 | raise ValueError(f"Unrecognized solver {solver}") 33 | 34 | for name, value in init_coeffs.items(): 35 | self.register_parameter(name, nn.Parameter(torch.tensor(value))) 36 | 37 | self.ode = ode 38 | self.var_names = ode.keys() 39 | self.init_vars = { 40 | name: torch.tensor(value, device=self.device) 41 | for name, value in init_vars.items() 42 | } 43 | self.coeffs = dict(self.named_parameters()) 44 | self.outvar = self.var_names if outvar is None else outvar 45 | 46 | # determines method of training 47 | self.observed = set(self.outvar) == set(self.var_names) 48 | 49 | self.dt = dt 50 | 51 | def _euler_step(self, prev_val): 52 | """Computes a single step of the ODE using Euler's method. 53 | 54 | Args: 55 | prev_val (dict): Previous values for each variable 56 | 57 | Returns: 58 | dict: Euler's method step prediction 59 | """ 60 | pred = {name: value.unsqueeze(0) for name, value in self.init_vars.items()} 61 | 62 | for var in self.var_names: 63 | pred[var] = prev_val[var] + self.ode[var](prev_val, self.coeffs) * self.dt 64 | 65 | return pred 66 | 67 | def _runge_kutta_4_step(self, prev_val): 68 | """Computes a single step of the ODE using a 4th order Runge-Kutta method. 69 | 70 | Args: 71 | prev_val (dict): Previous values for each variable 72 | 73 | Returns: 74 | dict: 4th order Runge-Kutta method step prediction 75 | """ 76 | pred = {name: value.unsqueeze(0) for name, value in self.init_vars.items()} 77 | 78 | k_1 = prev_val 79 | k_2 = {} 80 | k_3 = {} 81 | k_4 = {} 82 | 83 | for var in self.var_names: 84 | k_2[var] = prev_val[var] + self.ode[var](k_1, self.coeffs) * 0.5 * self.dt 85 | 86 | for var in self.var_names: 87 | k_3[var] = prev_val[var] + self.ode[var](k_2, self.coeffs) * 0.5 * self.dt 88 | 89 | for var in self.var_names: 90 | k_4[var] = prev_val[var] + self.ode[var](k_3, self.coeffs) * self.dt 91 | 92 | for var in self.var_names: 93 | result = self.ode[var](k_1, self.coeffs) / 6 94 | result += self.ode[var](k_2, self.coeffs) / 3 95 | result += self.ode[var](k_3, self.coeffs) / 3 96 | result += self.ode[var](k_4, self.coeffs) / 6 97 | pred[var] = prev_val[var] + result * self.dt 98 | 99 | return pred 100 | 101 | def solver(self, nt, initial=None): 102 | """Numerical simulation of the ODE using the selected solver method. 103 | 104 | Args: 105 | nt (int): Number of time-steps 106 | initial (dict): Initial values for each variable 107 | 108 | Returns: 109 | dict: Prediction of each variable after nt time steps 110 | """ 111 | if initial is None: 112 | initial = self.init_vars 113 | 114 | pred = {name: value.unsqueeze(0) for name, value in initial.items()} 115 | 116 | for n in range(nt - 1): 117 | # create dictionary containing values from previous time step 118 | prev_val = {var: pred[var][[n]] for var in self.var_names} 119 | new_val = self.step_solver(prev_val) 120 | 121 | for var in self.var_names: 122 | pred[var] = torch.cat([pred[var], new_val[var]]) 123 | 124 | # reformat output to contain desired (observed) variables 125 | return torch.stack([pred[var] for var in self.outvar], dim=1) 126 | 127 | def fit(self, x, y, max_epochs=10, batch_size=128): 128 | """Fits the model to the given data. 129 | 130 | Args: 131 | x (torch.Tensor): Input data 132 | y (torch.Tensor): Output data 133 | max_epochs (int): Number of training epochs 134 | batch_size (int): Batch size for torch.utils.data.DataLoader 135 | Set to x.shape[0] if unobserved variables are present 136 | """ 137 | if self.observed: 138 | super().fit(x, y, max_epochs, batch_size) 139 | else: 140 | super().fit(x, y, max_epochs, x.shape[0]) 141 | 142 | def forward(self, nt): 143 | return self.solver(nt) 144 | 145 | def get_coeffs(self): 146 | return {name: param.item() for name, param in self.named_parameters()} 147 | 148 | def _step(self, batch, batch_idx, num_batches): 149 | x, y = self.prepare_batch(batch) 150 | 151 | if self.observed: 152 | # retrieve numerical simulation of single time-steps for each datapoint 153 | self.zero_grad() 154 | init_point = {var: x[:, i] for i, var in enumerate(self.outvar)} 155 | pred = self.step_solver(init_point) 156 | predictions = torch.stack([pred[var] for var in self.outvar], dim=1) 157 | else: 158 | # retrieve numerical simulation of the whole dataset 159 | nt = x.shape[0] 160 | predictions = self(nt) 161 | 162 | loss = self.criterion(predictions, y) 163 | return loss 164 | 165 | def backward(self, loss, optimizer, optimizer_idx): 166 | # use retain_graph=True to mitigate RuntimeError 167 | loss.backward(retain_graph=True) 168 | -------------------------------------------------------------------------------- /torchts/nn/models/seq2seq.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch import nn 3 | 4 | from torchts.nn.model import TimeSeriesModel 5 | 6 | 7 | class Encoder(nn.Module): 8 | def __init__(self, input_dim, hidden_dim, num_layers, dropout_rate): 9 | """ 10 | Args: 11 | input_dim: the dimension of input sequences. 12 | hidden_dim: number hidden units. 13 | num_layers: number of encode layers. 14 | dropout_rate: recurrent dropout rate. 15 | """ 16 | super().__init__() 17 | self.num_layers = num_layers 18 | self.hidden_dim = hidden_dim 19 | self.lstm = nn.LSTM( 20 | input_dim, 21 | hidden_dim, 22 | num_layers=num_layers, 23 | bidirectional=True, 24 | dropout=dropout_rate, 25 | batch_first=True, 26 | ) 27 | 28 | def forward(self, source): 29 | """ 30 | Args: 31 | source: input tensor(batch_size*input dimension) 32 | Return: 33 | outputs: Prediction 34 | concat_hidden: hidden states 35 | """ 36 | outputs, hidden = self.lstm(source) 37 | return outputs, hidden 38 | 39 | 40 | class Decoder(nn.Module): 41 | def __init__(self, output_dim, hidden_dim, num_layers, dropout_rate): 42 | """ 43 | Args: 44 | output_dim: the dimension of output sequences. 45 | hidden_dim: number hidden units. 46 | num_layers: number of code layers. 47 | dropout_rate: recurrent dropout rate. 48 | """ 49 | super().__init__() 50 | 51 | # Since the encoder is bidirectional, decoder has double hidden size 52 | self.lstm = nn.LSTM( 53 | output_dim, 54 | hidden_dim * 2, 55 | num_layers=num_layers, 56 | dropout=dropout_rate, 57 | batch_first=True, 58 | ) 59 | 60 | self.out = nn.Linear(hidden_dim * 2, output_dim) 61 | 62 | def forward(self, x, hidden): 63 | """ 64 | Args: 65 | x: prediction from previous prediction. 66 | hidden: hidden states from previous cell. 67 | Returns: 68 | 1. prediction for current step. 69 | 2. hidden state pass to next cell. 70 | """ 71 | output, hidden = self.lstm(x, hidden) 72 | prediction = self.out(output) 73 | return prediction, hidden 74 | 75 | 76 | class Seq2Seq(TimeSeriesModel): 77 | def __init__(self, encoder, decoder, output_dim, horizon, **kwargs): 78 | """ 79 | Args: 80 | encoder: Encoder object. 81 | decoder: Decoder object. 82 | """ 83 | super().__init__(**kwargs) 84 | self.encoder = encoder 85 | self.decoder = decoder 86 | self.output_dim = output_dim 87 | self.horizon = horizon 88 | 89 | def forward(self, source, target=None, batches_seen=None): 90 | """ 91 | Args: 92 | source: input tensor. 93 | Returns: 94 | total prediction 95 | """ 96 | encoder_output, encoder_hidden = self.encoder(source) 97 | 98 | # Concatenate the hidden states of both directions. 99 | h = torch.cat( 100 | [ 101 | encoder_hidden[0][0 : self.encoder.num_layers, :, :], 102 | encoder_hidden[0][-self.encoder.num_layers :, :, :], 103 | ], 104 | dim=2, 105 | out=None, 106 | ) 107 | 108 | c = torch.cat( 109 | [ 110 | encoder_hidden[1][0 : self.encoder.num_layers, :, :], 111 | encoder_hidden[1][-self.encoder.num_layers :, :, :], 112 | ], 113 | dim=2, 114 | out=None, 115 | ) 116 | 117 | batch_size = source.size(0) 118 | shape = (batch_size, 1, self.output_dim) 119 | decoder_output = torch.zeros(shape, device=self.device) 120 | decoder_hidden = (h, c) 121 | outputs = [] 122 | 123 | for _ in range(self.horizon): 124 | decoder_output, decoder_hidden = self.decoder( 125 | decoder_output, decoder_hidden 126 | ) 127 | outputs.append(decoder_output) 128 | 129 | return torch.cat(outputs, dim=1) 130 | -------------------------------------------------------------------------------- /torchts/utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/torchts/utils/__init__.py -------------------------------------------------------------------------------- /torchts/utils/data.py: -------------------------------------------------------------------------------- 1 | import os 2 | import pickle 3 | from collections.abc import Iterable 4 | 5 | import numpy as np 6 | import torch 7 | from torch.utils.data import DataLoader, TensorDataset 8 | 9 | 10 | class PaddedDataset(TensorDataset): 11 | def __init__(self, batch_size, *data, pad_with_last_sample=True): 12 | data_pad = [None] * len(data) 13 | 14 | if pad_with_last_sample: 15 | num_padding = (batch_size - (len(data[0]) % batch_size)) % batch_size 16 | 17 | for i in range(len(data)): 18 | padding = np.repeat(data[i][-1:], num_padding, axis=0) 19 | data_pad[i] = np.concatenate([data[i], padding], axis=0) 20 | 21 | super().__init__(*(torch.from_numpy(d).float() for d in data_pad)) 22 | 23 | 24 | class StandardScaler: 25 | def __init__(self, data): 26 | self.mean = data.mean() 27 | self.std = data.std() 28 | 29 | def transform(self, data): 30 | return (data - self.mean) / self.std 31 | 32 | def inverse_transform(self, data): 33 | return (data * self.std) + self.mean 34 | 35 | 36 | def concat(a, b): 37 | return torch.cat([a, b.unsqueeze(0)], dim=0) 38 | 39 | 40 | def generate_ode_dataset(x): 41 | """Generates dataset for ODESolver when training with zero unobserved variables. 42 | 43 | Args: 44 | x (torch.Tensor): Original time series data 45 | 46 | Returns: 47 | torch.Tensor: Time series data from time step 0 to n-1 48 | torch.Tensor: Time series data from time step 1 to n 49 | """ 50 | n = x.shape[0] 51 | return x[: n - 1], x[1:] 52 | 53 | 54 | def load_dataset(dataset_dir, batch_size, val_batch_size=None, test_batch_size=None): 55 | if val_batch_size is None: 56 | val_batch_size = batch_size 57 | 58 | if test_batch_size is None: 59 | test_batch_size = batch_size 60 | 61 | data = {} 62 | 63 | for category in ["train", "val", "test"]: 64 | cat_data = np.load(os.path.join(dataset_dir, category + ".npz")) 65 | data["x_" + category] = cat_data["x"] 66 | data["y_" + category] = cat_data["y"] 67 | 68 | scaler = StandardScaler(data["x_train"][..., 0]) 69 | 70 | for category in ["train", "val", "test"]: 71 | data["x_" + category][..., 0] = scaler.transform(data["x_" + category][..., 0]) 72 | data["y_" + category][..., 0] = scaler.transform(data["y_" + category][..., 0]) 73 | 74 | data_train = PaddedDataset(batch_size, data["x_train"], data["y_train"]) 75 | data["train_loader"] = DataLoader(data_train, batch_size, shuffle=True) 76 | 77 | data_val = PaddedDataset(val_batch_size, data["x_val"], data["y_val"]) 78 | data["val_loader"] = DataLoader(data_val, val_batch_size, shuffle=False) 79 | 80 | data_test = PaddedDataset(test_batch_size, data["x_test"], data["y_test"]) 81 | data["test_loader"] = DataLoader(data_test, test_batch_size, shuffle=False) 82 | 83 | data["scaler"] = scaler 84 | 85 | return data 86 | 87 | 88 | def load_graph_data(pkl_filename): 89 | sensor_ids, sensor_id_to_ind, adj_mx = load_pickle(pkl_filename) 90 | return sensor_ids, sensor_id_to_ind, adj_mx 91 | 92 | 93 | def load_pickle(pickle_file): 94 | try: 95 | with open(pickle_file, "rb") as f: 96 | pickle_data = pickle.load(f) 97 | except UnicodeDecodeError: 98 | with open(pickle_file, "rb") as f: 99 | pickle_data = pickle.load(f, encoding="latin1") 100 | except Exception as e: 101 | print(f"Unable to load data {pickle_file} : {e}") 102 | raise e 103 | 104 | return pickle_data 105 | 106 | 107 | def sliding_window(tensor, lags, horizon=1, dim=0, step=1): 108 | is_int = isinstance(lags, int) 109 | is_iter = isinstance(lags, Iterable) and all(isinstance(lag, int) for lag in lags) 110 | 111 | if not is_int and not is_iter: 112 | raise TypeError("lags must be of type int or Iterable[int]") 113 | 114 | if (is_int and lags < 1) or (is_iter and any(lag < 1 for lag in lags)): 115 | raise ValueError(f"lags must be positive but found {lags}") 116 | 117 | if is_int: 118 | data = tensor.unfold(dim, lags + horizon, step) 119 | x, y = data[:, :lags], data[:, -1] 120 | else: 121 | data = tensor.unfold(dim, max(lags) + horizon, step) 122 | x, y = data[:, [lag - 1 for lag in lags]], data[:, -1] 123 | 124 | return x, y 125 | -------------------------------------------------------------------------------- /torchts/utils/graph.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import scipy.sparse as sp 3 | import torch 4 | from scipy.sparse import linalg 5 | 6 | 7 | def normalized_laplacian(adj): 8 | adj = sp.coo_matrix(adj) 9 | d = np.array(adj.sum(1)) 10 | d_inv_sqrt = np.power(d, -0.5).flatten() 11 | d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.0 12 | d_mat_inv_sqrt = sp.diags(d_inv_sqrt) 13 | I = sp.eye(adj.shape[0]) 14 | L = I - adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() 15 | return L 16 | 17 | 18 | def random_walk(adj_mx): 19 | adj_mx = sp.coo_matrix(adj_mx) 20 | d = np.array(adj_mx.sum(1)) 21 | d_inv = np.power(d, -1).flatten() 22 | d_inv[np.isinf(d_inv)] = 0.0 23 | d_mat_inv = sp.diags(d_inv) 24 | random_walk_mx = d_mat_inv.dot(adj_mx).tocoo() 25 | return random_walk_mx 26 | 27 | 28 | def reverse_random_walk(adj_mx): 29 | return random_walk(np.transpose(adj_mx)) 30 | 31 | 32 | def scaled_laplacian(adj_mx, lambda_max=2, undirected=True): 33 | if sp.issparse(adj_mx): 34 | adj_mx = adj_mx.todense() 35 | 36 | if undirected: 37 | adj_mx = np.maximum.reduce([adj_mx, adj_mx.T]) 38 | 39 | L = normalized_laplacian(adj_mx) 40 | 41 | if lambda_max is None: 42 | lambda_max, _ = linalg.eigsh(L, 1, which="LM") 43 | lambda_max = lambda_max[0] 44 | 45 | L = sp.csr_matrix(L) 46 | M, _ = L.shape 47 | I = sp.identity(M, format="csr", dtype=L.dtype) 48 | L = (2 / lambda_max * L) - I 49 | return L 50 | 51 | 52 | def sparse_matrix(L): 53 | L = L.tocoo() 54 | indices = np.column_stack((L.row, L.col)) 55 | # this is to ensure row-major ordering to equal torch.sparse.sparse_reorder(L) 56 | indices = indices[np.lexsort((indices[:, 0], indices[:, 1]))] 57 | L = torch.sparse_coo_tensor(indices.T, L.data, L.shape) 58 | return L 59 | -------------------------------------------------------------------------------- /website/.gitignore: -------------------------------------------------------------------------------- 1 | # Dependencies 2 | /node_modules 3 | yarn.lock 4 | 5 | # Production 6 | /build 7 | /static/api 8 | 9 | # Generated files 10 | .docusaurus 11 | .cache-loader 12 | 13 | # Misc 14 | .DS_Store 15 | .env.local 16 | .env.development.local 17 | .env.test.local 18 | .env.production.local 19 | 20 | npm-debug.log* 21 | yarn-debug.log* 22 | yarn-error.log* 23 | -------------------------------------------------------------------------------- /website/README.md: -------------------------------------------------------------------------------- 1 | # Website 2 | 3 | This website is built using [Docusaurus 2](https://docusaurus.io/), a modern static website generator. 4 | 5 | ## Installation 6 | 7 | ```console 8 | yarn install 9 | ``` 10 | 11 | ## Local Development 12 | 13 | ```console 14 | yarn start 15 | ``` 16 | 17 | This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server. 18 | 19 | ## Build 20 | 21 | ```console 22 | yarn build 23 | ``` 24 | 25 | This command generates static content into the `build` directory and can be served using any static contents hosting service. 26 | 27 | ## Deployment 28 | 29 | ```console 30 | GIT_USER= USE_SSH=true yarn deploy 31 | ``` 32 | 33 | If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch. 34 | -------------------------------------------------------------------------------- /website/babel.config.js: -------------------------------------------------------------------------------- 1 | module.exports = { 2 | presets: [require.resolve('@docusaurus/core/lib/babel/preset')], 3 | } 4 | -------------------------------------------------------------------------------- /website/docs/deep-sequence-to-sequence-models/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "Deep Sequence to Sequence Models", 3 | "position": 3 4 | } 5 | -------------------------------------------------------------------------------- /website/docs/deep-sequence-to-sequence-models/seq2seq-gru.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Seq2Seq with GRU 3 | slug: /seq2seq-gru 4 | --- 5 | 6 | The [sequence to sequence model](https://proceedings.neurips.cc/paper/2014/file/a14ac55a4f27472c5d894ec1c3c743d2-Paper.pdf) originates from language translation. Our implementation adapts the model for multi-step time series forecasting. Specifically, given the input series $x_1, \ldots, x_{t}$, the model maps the input series to the output series: 7 | 8 | $$ 9 | x_{t-p}, x_{t-p+1}, \ldots, x_{t-1} \longrightarrow x_t, x_{t+1}, \ldots, x_{t+h-1} 10 | $$ 11 | 12 | where $p$ is the input history length and $h$ is the forecasting horizon. 13 | 14 | Sequence to sequence (Seq2Seq) models consist of an encoder and a decoder. The final state of the encoder is fed as the initial state of the decoder. We can use various models for both the encoder and decoder. This function implements a Gated Recurrent Unit (GRU). 15 | -------------------------------------------------------------------------------- /website/docs/deep-sequence-to-sequence-models/seq2seq-lstm.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Seq2Seq with LSTM 3 | slug: /seq2seq-lstm 4 | --- 5 | 6 | The [sequence to sequence model](https://proceedings.neurips.cc/paper/2014/file/a14ac55a4f27472c5d894ec1c3c743d2-Paper.pdf) originates from language translation. Our implementation adapts the model for multi-step time series forecasting. Specifically, given the input series $x_1, \ldots, x_{t}$, the model maps the input series to the output series: 7 | 8 | $$ 9 | x_{t-p}, x_{t-p+1}, \ldots, x_{t-1} \longrightarrow x_t, x_{t+1}, \ldots, x_{t+h-1} 10 | $$ 11 | 12 | where $p$ is the input history length and $h$ is the forecasting horizon. 13 | 14 | Sequence to sequence (Seq2Seq) models consist of an encoder and a decoder. The final state of the encoder is fed as the initial state of the decoder. We can use various models tor both the encoder and decoder. This function implements a Long Short Term Memory (LSTM). 15 | -------------------------------------------------------------------------------- /website/docs/deep-spatiotemporal-models/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "Deep Spatiotemporal Models", 3 | "position": 4 4 | } 5 | -------------------------------------------------------------------------------- /website/docs/deep-spatiotemporal-models/convolutional-lstm.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Convolutional LSTM 3 | slug: /convolutional-lstm 4 | --- 5 | 6 | In spatiotemporal forecasting, assume we have multiple time series generated from a fixed space $x(s,t)$. [Convolutional LSTM](https://papers.nips.cc/paper/2015/file/07563a3fe3bbe7e3ba84431ad9d055af-Paper.pdf) models the time series on a regular grid, similar to a video. 7 | 8 | Convolutional LSTM replaces the matrix multiplication in a regular LSTM with convolution. It determines the future state of a certain cell in the grid by the inputs and past states of its local neighbors: 9 | 10 | $$ 11 | \begin{bmatrix} i_t \\ f_t \\ o_t \end{bmatrix} = \sigma\big(W^{x} \star x_t + W^h \star h_{t-1} + W^c \circ c_{t-1} + b\big) 12 | $$ 13 | -------------------------------------------------------------------------------- /website/docs/deep-spatiotemporal-models/diffusion-convolutional-lstm.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Diffusion Convolutional LSTM 3 | slug: /diffusion-convolutional-lstm 4 | --- 5 | 6 | In spatiotemporal forecasting, assume we have multiple time series generated from a fixed space $x(s,t)$. [Diffusion Convolutional LSTM](https://openreview.net/pdf?id=SJiHXGWAZ) models the time series on an irregular grid (graph) as a diffusion process. 7 | 8 | Diffusion Convolutional LSTM replaces the matrix multiplication in a regular LSTM with diffusion convolution. It determines the future state of a certain cell in the graph by the inputs and past states of its local neighbors: 9 | 10 | $$ 11 | \begin{bmatrix} i_t \\ f_t \\ o_t \end{bmatrix} = \sigma\big(W^{x} \star_g x_t + W^h \star_g h_{t-1} + W^c \circ c_{t-1} + b\big) 12 | $$ 13 | 14 | where $W \star_g x = \sum_{i=1}^k \big(D^{-1}A\big)^i \cdot W \cdot x$ is the diffusion convolution. 15 | -------------------------------------------------------------------------------- /website/docs/intro.md: -------------------------------------------------------------------------------- 1 | --- 2 | sidebar_position: 1 3 | title: Introduction 4 | slug: / 5 | --- 6 | 7 | Time series is one of the fastest growing and richest types of data. In a variety of domains including dynamical systems, healthcare, climate science and economics, there have been increasing amounts of complex dynamic data due to a shift away from parsimonious, infrequent measurements to nearly continuous real-time monitoring and recording. This burgeoning amount of new data calls for novel theoretical and algorithmic tools and insights. 8 | 9 | [TorchTS](https://rose-stl-lab.github.io/torchTS/) is a deep learning library for time series forecasting built on [Pytorch](https://pytorch.org/). The tools in this library mostly come out the research from [Spatiotemporal Machine Learning Lab](https://github.com/Rose-STL-Lab). The library is designed with minimal dependencies and user-friendly interfaces. A particular emphasis of the library is scalability, which exploits auto-differentiation and various inductive biases in the data. 10 | -------------------------------------------------------------------------------- /website/docs/linear-time-series-models/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "Linear Time Series Models", 3 | "position": 2 4 | } 5 | -------------------------------------------------------------------------------- /website/docs/linear-time-series-models/autoregressive.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Autoregressive Model (AR) 3 | slug: /autoregressive 4 | --- 5 | 6 | Given a time series $x_1, \ldots, x_t$, a $p$-th order autoregressive model (denoted AR($p$)) is defined as a linear function of the input series $x$: 7 | 8 | $$ 9 | x_t = \sum_{i=1}^p a_i x_{t-i} + e_t 10 | $$ 11 | 12 | where $\{a_i\}$ are the model coefficients and the series $\{e_t\}$ can represent either a controlled external input or noise. Note that the expression 13 | 14 | $$ 15 | \sum_{i=1}^p a_i x_{t-i} = a_1 x_{t-1} + a_2 x_{t-2} + \cdots + a_p x_{t-p} 16 | $$ 17 | 18 | describes a convolution filter. We can implement AR($p$) using either a feedforward neural network with a rolling window or a convolutional network on the entire series. 19 | -------------------------------------------------------------------------------- /website/docs/linear-time-series-models/vector-autoregressive.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Vector Autoregressive Model (VAR) 3 | slug: /vector-autoregressive 4 | --- 5 | 6 | Given $k$ time series $x_1, \ldots, x_t$ with $x_t \in \mathbb{R}^k$, a $p$-th order vector autoregressive model (denoted VAR($p$)) generalizes the univariate AR model. It models the output as linear functions of the input series $x$: 7 | 8 | $$ 9 | x_t = \sum_{i=1}^p A_i x_{t-i} + e_t 10 | $$ 11 | 12 | where $A_i \in \mathbb{R}^{k{\times}k}$ is a $k{\times}k$ matrix. The series $\{e_t\}$ can represent either a controlled external input or noise. 13 | -------------------------------------------------------------------------------- /website/docs/physics-guided-deep-sequence-models/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "Physics-Guided Deep Sequence Models", 3 | "position": 5 4 | } 5 | -------------------------------------------------------------------------------- /website/docs/physics-guided-deep-sequence-models/autoode.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: AutoODE 3 | slug: /autoode 4 | --- 5 | 6 | Assume the time series $x_t \in \mathbb{R}^d$ is governed by unknown differential equations: 7 | 8 | $$ 9 | \begin{aligned} 10 | &\frac{dx}{dt} = f_\theta(t, x, u) \\ 11 | &\frac{du}{dt} = g_\theta(t, x, u) \\ 12 | &x(t_0) = x_0 \\ 13 | &u(t_0) = u_0 14 | \end{aligned} 15 | $$ 16 | 17 | where $u \in \mathbb{R}^p$ are the unobserved variables. [AutoODE](https://arxiv.org/pdf/2011.10616.pdf) uses auto-differentiation to estimate the parameters $\theta$ of the equations. 18 | -------------------------------------------------------------------------------- /website/docs/physics-guided-deep-sequence-models/hybrid-autoode.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Hybrid AutoODE 3 | slug: /hybrid-autoode 4 | --- 5 | 6 | Assume the time series $x_t \in \mathbb{R}^d$ is governed by unknown differential equations and by other unknown factors that could affect its trajectory. The Hybrid AutoODE uses physics-guided models in conjunction with neural networks to improve the prediction of $x_t$. It is modelled by the following equations: 7 | 8 | $$ 9 | \begin{aligned} 10 | &\frac{dx}{dt} = f_\theta(t, x, u, F) \\ 11 | &\frac{du}{dt} = g_\theta(t, x, u, F) \\ 12 | &x(t_0) = x_0 \\ 13 | &u(t_0) = u_0 14 | \end{aligned} 15 | $$ 16 | 17 | where $u \in \mathbb{R}^p$ are the unobserved variables and $F$ is a neural network. The Hybrid AutoODE uses auto-differentiation to estimate the parameters $\theta$ of the equations and the neural network. 18 | -------------------------------------------------------------------------------- /website/docs/uncertainty-quantification-methods/_category_.json: -------------------------------------------------------------------------------- 1 | { 2 | "label": "Uncertainty Quantification Methods", 3 | "position": 6 4 | } 5 | -------------------------------------------------------------------------------- /website/docs/uncertainty-quantification-methods/mean-interval-score-regression.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: MIS Regression 3 | slug: /mis-regression 4 | --- 5 | 6 | Mean interval score (MIS) regression directly minimizes MIS, a scoring function for predictions of confidence intervals. This is done by using MIS as the loss function for deep neural networks. The formula to calculate MIS is also known as Winkler loss and is expressed as: 7 | 8 | $$ 9 | MIS = \frac{1}{h}\sum_{j=1}^{h}\left((u_{t+j} - l_{t+j}) + \frac{2}{\alpha}(l_{t+j} - y_{t+j})\mathbb{1}(y_{t+j} < l_{t+j}) + \frac{2}{\alpha}(y_{t+j} - u_{t+j})\mathbb{1}(y_{t+j} > u_{t+j})\right) 10 | $$ 11 | 12 | where $u$ and $l$ are the upper and lower bounds respectively, and $\alpha$ is a fixed confidence level. Here $\alpha$ is equivalent to $1-CI$, where $CI$ is the desired confidence interval. Therefore, $\alpha=0.05$ for a $95\%$ confidence interval. There are 3 parts to this loss function, which summed together equal the total mean interval score. 13 | 14 | 1. Penalize distance between the upper and lower bounds. 15 | 2. Penalize distance between the lower bound and actual value by a ratio of $2/\alpha$ when the actual value is lower than the lower bound. 16 | 3. Penalize distance between the actual value and upper bound by a ratio of $2/\alpha$ when the actual value is higher than the upper bound. 17 | 18 | Since the loss function for this model jointly includes the upper and lower bounds, the result outputs both, unlike with quantile regression. 19 | -------------------------------------------------------------------------------- /website/docs/uncertainty-quantification-methods/quantile-regression.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Quantile Regression 3 | slug: /quantile-regression 4 | --- 5 | 6 | Quantile regression uses the one-sided quantile loss to predict specific percentiles of the dependent variable. The quantile regression model uses the pinball loss function written as: 7 | 8 | $$ 9 | L_{Quantile}\big(y,f(x),\theta,p\big) = min_\theta\{\mathbb{E}_{(x,y)\sim D}[(y - f(x))(p - \mathbb{1}\{y < f(x)\})]\} 10 | $$ 11 | 12 | where $p$ is our fixed confidence interval parameterized by $\theta$. When the pinball loss is minimized, the result is the optimal quantile. 13 | -------------------------------------------------------------------------------- /website/docs/uncertainty-quantification-methods/stochastic-gradient-mcmc.md: -------------------------------------------------------------------------------- 1 | --- 2 | title: Stochastic Gradient MCMC (coming soon) 3 | slug: /stochastic-gradient-mcmc 4 | --- 5 | 6 | Stochastic gradient Markov chain Monte Carlo (SG-MCMC) is a Bayesian uncertainty quantification method. This form of gradient descent is useful in calculating quantiles according to subsets of the training data, which are selected based on the posterior distribution over the parameter space. This implementation also follows the stochastic gradient thermostat method (SGNHT), whose purpose is to control gradient noise, which keeps the distribution Gaussian. We generate samples of model parameters $\theta$ as a function of the loss function $L(\theta)$, diffusion coefficients $A$, and learning rate $h$, in addition to auxiliary variables $p \in \mathbb{R}^d$ and $\zeta \in \mathbb{R}^d$. The values of $\theta$, $p$, and $\zeta$ are randomly initialized and updated according to the rule: 7 | 8 | $$ 9 | \begin{aligned} 10 | \theta_{k+1} &= \theta_k + p_kh \\ 11 | p_{k+1} &= p_k - \triangledown L(\theta)h - \zeta_kp_kh + \mathcal{N}(0,2Ah) \\ 12 | \zeta_{k+1} &= \zeta_k + \left(\frac{p^t_kp_k}{d} - 1\right)h 13 | \end{aligned} 14 | $$ 15 | 16 | where after the $k$th iteration, $\theta$ follows the distribution of the posterior. We quantify the uncertainty of our prediction by running for multiple $\theta$ with different samples according to the posterior. 17 | -------------------------------------------------------------------------------- /website/docusaurus.config.js: -------------------------------------------------------------------------------- 1 | const lightCodeTheme = require("prism-react-renderer/themes/github"); 2 | const darkCodeTheme = require("prism-react-renderer/themes/dracula"); 3 | const math = require("remark-math"); 4 | const katex = require("rehype-katex"); 5 | 6 | /** @type {import('@docusaurus/types').DocusaurusConfig} */ 7 | module.exports = { 8 | title: "TorchTS", 9 | tagline: "Time series forecasting with PyTorch", 10 | url: "https://rose-stl-lab.github.io", 11 | baseUrl: "/torchTS/", 12 | onBrokenLinks: "throw", 13 | onBrokenMarkdownLinks: "warn", 14 | favicon: "img/logo2.png", 15 | scripts: [ 16 | "https://buttons.github.io/buttons.js", 17 | "https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.0/clipboard.min.js", 18 | ], 19 | stylesheets: [ 20 | "https://fonts.googleapis.com/css?family=IBM+Plex+Mono:500,700|Source+Code+Pro:500,700|Source+Sans+Pro:400,400i,700", 21 | { 22 | href: "https://cdn.jsdelivr.net/npm/katex@0.13.11/dist/katex.min.css", 23 | integrity: 24 | "sha384-Um5gpz1odJg5Z4HAmzPtgZKdTBHZdw8S29IecapCSB31ligYPhHQZMIlWLYQGVoc", 25 | crossorigin: "anonymous", 26 | }, 27 | ], 28 | 29 | organizationName: "Rose-STL-Lab", // Usually your GitHub org/user name. 30 | projectName: "torchTS", // Usually your repo name. 31 | themeConfig: { 32 | // colorMode: { 33 | // defaultMode: "light", 34 | // disableSwitch: true, 35 | // }, 36 | navbar: { 37 | title: "TorchTS", 38 | logo: { 39 | alt: "My Site Logo", 40 | src: "img/logo2.png", 41 | }, 42 | items: [ 43 | { 44 | type: "doc", 45 | docId: "intro", 46 | position: "left", 47 | label: "Docs", 48 | }, 49 | { 50 | href: "https://rose-stl-lab.github.io/torchTS/api", 51 | label: "API Reference", 52 | position: "left", 53 | }, 54 | { 55 | href: "https://github.com/Rose-STL-Lab/torchTS", 56 | label: "GitHub", 57 | position: "right", 58 | }, 59 | ], 60 | }, 61 | footer: { 62 | links: [ 63 | { 64 | title: "Docs", 65 | items: [ 66 | { 67 | label: "Getting Started", 68 | to: "docs", 69 | }, 70 | // { 71 | // label: 'Tutorials', 72 | // to: '/tutorials', 73 | // }, 74 | // { 75 | // label: 'API', 76 | // to: '/api', 77 | // }, 78 | ], 79 | }, 80 | { 81 | title: "Community", 82 | items: [ 83 | { 84 | label: "Slack", 85 | href: "https://github.com/Rose-STL-Lab/torchTS", 86 | }, 87 | { 88 | label: "Discord", 89 | href: "https://github.com/Rose-STL-Lab/torchTS", 90 | }, 91 | ], 92 | }, 93 | { 94 | title: "More", 95 | items: [ 96 | { 97 | html: ` 98 | Star 105 | `, 106 | }, 107 | { 108 | label: "GitHub", 109 | href: "https://github.com/Rose-STL-Lab/torchTS", 110 | }, 111 | { 112 | label: "Edit Docs on GitHub", 113 | href: "https://github.com/Rose-STL-Lab/torchTS", 114 | }, 115 | ], 116 | }, 117 | ], 118 | copyright: `Copyright © ${new Date().getFullYear()} TorchTS Team`, 119 | logo: { 120 | src: "img/octopus-128x128.png", 121 | }, 122 | }, 123 | prism: { 124 | theme: lightCodeTheme, 125 | darkTheme: darkCodeTheme, 126 | }, 127 | fonts: { 128 | fontMain: ["Source Sans Pro", "sans-serif"], 129 | fontCode: ["IBM Plex Mono", "monospace"], 130 | }, 131 | }, 132 | presets: [ 133 | [ 134 | "@docusaurus/preset-classic", 135 | { 136 | docs: { 137 | sidebarPath: require.resolve("./sidebars.js"), 138 | remarkPlugins: [math], 139 | showLastUpdateAuthor: true, 140 | showLastUpdateTime: true, 141 | rehypePlugins: [katex], 142 | // Please change this to your repo. 143 | editUrl: "https://github.com/Rose-STL-Lab/torchTS/edit/main/website/", 144 | }, 145 | theme: { 146 | customCss: require.resolve("./src/css/custom.css"), 147 | }, 148 | }, 149 | ], 150 | ], 151 | }; 152 | -------------------------------------------------------------------------------- /website/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "website", 3 | "version": "0.0.0", 4 | "private": true, 5 | "scripts": { 6 | "docusaurus": "docusaurus", 7 | "start": "docusaurus start", 8 | "build": "docusaurus build", 9 | "swizzle": "docusaurus swizzle", 10 | "deploy": "docusaurus deploy", 11 | "clear": "docusaurus clear", 12 | "serve": "docusaurus serve", 13 | "write-translations": "docusaurus write-translations", 14 | "write-heading-ids": "docusaurus write-heading-ids" 15 | }, 16 | "dependencies": { 17 | "@docusaurus/core": "2.0.0-beta.3", 18 | "@docusaurus/preset-classic": "2.0.0-beta.3", 19 | "@mdx-js/react": "^1.6.21", 20 | "@svgr/webpack": "^5.5.0", 21 | "classnames": "^2.3.1", 22 | "clsx": "^1.1.1", 23 | "file-loader": "^6.2.0", 24 | "hast-util-is-element": "^1.1.0", 25 | "prism-react-renderer": "^1.2.1", 26 | "react": "^17.0.1", 27 | "react-dom": "^17.0.1", 28 | "rehype-katex": "^4.0.0", 29 | "remark-math": "^3.0.1", 30 | "remarkable": "^2.0.1", 31 | "url-loader": "^4.1.1" 32 | }, 33 | "browserslist": { 34 | "production": [ 35 | ">0.5%", 36 | "not dead", 37 | "not op_mini all" 38 | ], 39 | "development": [ 40 | "last 1 chrome version", 41 | "last 1 firefox version", 42 | "last 1 safari version" 43 | ] 44 | } 45 | } 46 | -------------------------------------------------------------------------------- /website/sidebars.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Creating a sidebar enables you to: 3 | - create an ordered group of docs 4 | - render a sidebar for each doc of that group 5 | - provide next/previous navigation 6 | 7 | The sidebars can be generated from the filesystem, or explicitly defined here. 8 | 9 | Create as many sidebars as you want. 10 | */ 11 | 12 | module.exports = { 13 | // By default, Docusaurus generates a sidebar from the docs folder structure 14 | tutorialSidebar: [{ type: 'autogenerated', dirName: '.' }], 15 | 16 | // But you can create a sidebar manually 17 | /* 18 | tutorialSidebar: [ 19 | { 20 | type: 'category', 21 | label: 'Tutorial', 22 | items: ['hello'], 23 | }, 24 | ], 25 | */ 26 | } 27 | -------------------------------------------------------------------------------- /website/src/components/Container/index.jsx: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) 2017-present, Facebook, Inc. 3 | * 4 | * This source code is licensed under the MIT license found in the 5 | * LICENSE file in the root directory of this source tree. 6 | */ 7 | 8 | import React from 'react' 9 | import classNames from 'classnames' 10 | 11 | export const Container = props => { 12 | const containerClasses = classNames('block-container', props.className, { 13 | darkBackground: props.background === 'dark', 14 | highlightBackground: props.background === 'highlight', 15 | lightBackground: props.background === 'light', 16 | paddingAll: props.padding.indexOf('all') >= 0, 17 | paddingBottom: props.padding.indexOf('bottom') >= 0, 18 | paddingLeft: props.padding.indexOf('left') >= 0, 19 | paddingRight: props.padding.indexOf('right') >= 0, 20 | paddingTop: props.padding.indexOf('top') >= 0, 21 | }) 22 | let wrappedChildren 23 | 24 | if (props.wrapper) { 25 | wrappedChildren =
{props.children}
26 | } else { 27 | wrappedChildren = props.children 28 | } 29 | return ( 30 |
31 | {wrappedChildren} 32 |
33 | ) 34 | } 35 | 36 | Container.defaultProps = { 37 | background: null, 38 | padding: [], 39 | wrapper: true, 40 | } 41 | -------------------------------------------------------------------------------- /website/src/components/GridBlock/index.jsx: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) 2017-present, Facebook, Inc. 3 | * 4 | * This source code is licensed under the MIT license found in the 5 | * LICENSE file in the root directory of this source tree. 6 | */ 7 | 8 | import React from 'react' 9 | import classNames from 'classnames' 10 | import {MarkdownBlock} from '../MarkdownBlock' 11 | 12 | const renderBlockImage = (image, imageLink, imageAlt) => { 13 | if (!image) { 14 | return null 15 | } 16 | 17 | return ( 18 |
19 | {imageLink ? ( 20 | 21 | {imageAlt} 22 | 23 | ) : ( 24 | {imageAlt} 25 | )} 26 |
27 | ) 28 | } 29 | 30 | const renderBlockTitle = title => { 31 | if (!title) { 32 | return null 33 | } 34 | 35 | return ( 36 |

37 | {title} 38 |

39 | ) 40 | } 41 | 42 | export const GridBlock = props => { 43 | const renderBlock = origBlock => { 44 | const blockDefaults = { 45 | imageAlign: 'left', 46 | } 47 | 48 | const block = { 49 | ...blockDefaults, 50 | ...origBlock, 51 | } 52 | 53 | const blockClasses = classNames('blockElement', props.className, { 54 | alignCenter: props.align === 'center', 55 | alignRight: props.align === 'right', 56 | fourByGridBlock: props.layout === 'fourColumn', 57 | imageAlignSide: 58 | block.image && 59 | (block.imageAlign === 'left' || block.imageAlign === 'right'), 60 | imageAlignTop: block.image && block.imageAlign === 'top', 61 | imageAlignRight: block.image && block.imageAlign === 'right', 62 | imageAlignBottom: block.image && block.imageAlign === 'bottom', 63 | imageAlignLeft: block.image && block.imageAlign === 'left', 64 | threeByGridBlock: props.layout === 'threeColumn', 65 | twoByGridBlock: props.layout === 'twoColumn', 66 | }) 67 | 68 | const topLeftImage = 69 | (block.imageAlign === 'top' || block.imageAlign === 'left') && 70 | renderBlockImage(block.image, block.imageLink, block.imageAlt) 71 | 72 | const bottomRightImage = 73 | (block.imageAlign === 'bottom' || block.imageAlign === 'right') && 74 | renderBlockImage(block.image, block.imageLink, block.imageAlt) 75 | 76 | return ( 77 |
78 | {topLeftImage} 79 |
80 | {renderBlockTitle(block.title)} 81 | {block.content} 82 |
83 | {bottomRightImage} 84 |
85 | ) 86 | } 87 | 88 | return ( 89 |
{props.contents.map(renderBlock, this)}
90 | ) 91 | } 92 | 93 | GridBlock.defaultProps = { 94 | align: 'left', 95 | contents: [], 96 | layout: 'twoColumn', 97 | } 98 | -------------------------------------------------------------------------------- /website/src/components/HomepageFeatures.js: -------------------------------------------------------------------------------- 1 | import React from 'react' 2 | import clsx from 'clsx' 3 | import styles from './HomepageFeatures.module.css' 4 | 5 | const FeatureList = [ 6 | { 7 | title: 'Easy to Use', 8 | Svg: require('../../static/img/undraw_docusaurus_mountain.svg').default, 9 | description: ( 10 | <> 11 | Docusaurus was designed from the ground up to be easily installed and used to 12 | get your website up and running quickly. 13 | 14 | ), 15 | }, 16 | { 17 | title: 'Focus on What Matters', 18 | Svg: require('../../static/img/undraw_docusaurus_tree.svg').default, 19 | description: ( 20 | <> 21 | Docusaurus lets you focus on your docs, and we'll do the chores. Go ahead 22 | and move your docs into the docs directory. 23 | 24 | ), 25 | }, 26 | { 27 | title: 'Powered by React', 28 | Svg: require('../../static/img/undraw_docusaurus_react.svg').default, 29 | description: ( 30 | <> 31 | Extend or customize your website layout by reusing React. Docusaurus can be 32 | extended while reusing the same header and footer. 33 | 34 | ), 35 | }, 36 | ] 37 | 38 | function Feature({ Svg, title, description }) { 39 | return ( 40 |
41 |
42 | 43 |
44 |
45 |

{title}

46 |

{description}

47 |
48 |
49 | ) 50 | } 51 | 52 | export default function HomepageFeatures() { 53 | return ( 54 |
55 |
56 |
57 | {FeatureList.map((props, idx) => ( 58 | 59 | ))} 60 |
61 |
62 |
63 | ) 64 | } 65 | -------------------------------------------------------------------------------- /website/src/components/HomepageFeatures.module.css: -------------------------------------------------------------------------------- 1 | /* stylelint-disable docusaurus/copyright-header */ 2 | 3 | .features { 4 | display: flex; 5 | align-items: center; 6 | padding: 2rem 0; 7 | width: 100%; 8 | } 9 | 10 | .featureSvg { 11 | height: 200px; 12 | width: 200px; 13 | } 14 | -------------------------------------------------------------------------------- /website/src/components/MarkdownBlock/index.jsx: -------------------------------------------------------------------------------- 1 | import React from 'react' 2 | import {Remarkable} from 'remarkable' 3 | const md = new Remarkable() 4 | 5 | export const MarkdownBlock = ({children}) => { 6 | const markdown = md.render(children) 7 | return
8 | } 9 | -------------------------------------------------------------------------------- /website/src/css/custom.css: -------------------------------------------------------------------------------- 1 | :root { 2 | --ifm-light-background-color: var(--ifm-color-emphasis-100); 3 | } 4 | 5 | html[data-theme='dark'] { 6 | --ifm-link-color: #679df5; 7 | } 8 | 9 | .wrapper { 10 | margin: 0 auto; 11 | max-width: 1100px; 12 | padding: 0 20px; 13 | } 14 | 15 | .center { 16 | display: block; 17 | } 18 | 19 | .center, 20 | .homeContainer { 21 | text-align: center; 22 | } 23 | 24 | .homeContainer .homeWrapper { 25 | padding: 2em 10px; 26 | } 27 | 28 | .homeContainer .homeWrapper .wrapper { 29 | margin: 0 auto; 30 | max-width: 900px; 31 | padding: 0 20px; 32 | } 33 | 34 | .homeContainer .homeWrapper .projectLogo img { 35 | height: 200px; 36 | margin-bottom: 0; 37 | } 38 | 39 | .homeContainer .homeWrapper #project_title { 40 | font-size: 300%; 41 | letter-spacing: -0.08em; 42 | line-height: 1em; 43 | margin-bottom: 80px; 44 | } 45 | 46 | .homeContainer .homeWrapper #project_tagline { 47 | font-size: 200%; 48 | letter-spacing: -0.04em; 49 | line-height: 1em; 50 | } 51 | 52 | .projectLogo { 53 | display: none; 54 | pointer-events: none; 55 | } 56 | 57 | .projectLogo img { 58 | height: 100px; 59 | margin-bottom: 0; 60 | } 61 | 62 | .projectTitle { 63 | font-size: 250%; 64 | line-height: 1em; 65 | } 66 | 67 | @media only screen and (min-width: 480px) { 68 | .projectTitle { 69 | font-size: 300%; 70 | margin: 0.3em 0; 71 | } 72 | 73 | .projectLogo img { 74 | height: 200px; 75 | margin-bottom: 10px; 76 | } 77 | 78 | .homeContainer .homeWrapper { 79 | padding-left: 10px; 80 | padding-right: 10px; 81 | } 82 | } 83 | 84 | @media only screen and (min-width: 736px) { 85 | .homeContainer .homeWrapper { 86 | position: relative; 87 | } 88 | 89 | .homeContainer .homeWrapper #inner { 90 | max-width: 600px; 91 | padding-right: 40px; 92 | } 93 | } 94 | 95 | @media only screen and (min-width: 1200px) { 96 | .homeContainer .homeWrapper #inner { 97 | max-width: 750px; 98 | } 99 | 100 | .homeContainer .homeWrapper .projectLogo { 101 | align-items: center; 102 | bottom: 0; 103 | display: flex; 104 | justify-content: flex-end; 105 | left: 0; 106 | padding: 2em 100px 4em; 107 | position: absolute; 108 | right: 0; 109 | top: 0; 110 | } 111 | 112 | .homeContainer .homeWrapper .projectLogo img { 113 | height: 100%; 114 | max-height: 250px; 115 | } 116 | } 117 | 118 | @media only screen and (min-width: 1500px) { 119 | .homeContainer .homeWrapper #inner { 120 | max-width: 1100px; 121 | padding-bottom: 40px; 122 | padding-top: 40px; 123 | } 124 | 125 | .wrapper { 126 | max-width: 1400px; 127 | } 128 | } 129 | 130 | .mainContainer { 131 | flex: 1 1 0%; 132 | max-width: 100%; 133 | padding: 40px 0; 134 | } 135 | 136 | .mainContainer .wrapper { 137 | text-align: left; 138 | } 139 | 140 | .gridBlock { 141 | padding: 0; 142 | } 143 | 144 | .gridBlock > * { 145 | box-sizing: border-box; 146 | } 147 | 148 | .gridBlock .fourByGridBlock img, 149 | .gridBlock .threeByGridBlock img, 150 | .gridBlock .twoByGridBlock img { 151 | max-width: 100%; 152 | } 153 | 154 | .gridBlock .gridClear { 155 | clear: both; 156 | } 157 | 158 | @media only screen and (max-width: 735px) { 159 | .gridBlock .fourByGridBlock { 160 | flex: 1 0 26%; 161 | } 162 | } 163 | 164 | @media only screen and (min-width: 736px) { 165 | .gridBlock { 166 | display: flex; 167 | flex-direction: row; 168 | flex-wrap: wrap; 169 | } 170 | 171 | .gridBlock > * { 172 | margin: 0 12px; 173 | } 174 | 175 | .gridBlock > :first-child { 176 | margin-left: 0; 177 | } 178 | 179 | .gridBlock > :last-child { 180 | margin-right: 0; 181 | } 182 | 183 | .gridBlock .twoByGridBlock { 184 | flex: 1 0 40%; 185 | } 186 | 187 | .gridBlock .threeByGridBlock { 188 | flex: 1 0 26%; 189 | } 190 | 191 | .gridBlock .fourByGridBlock { 192 | flex: 1 0 20%; 193 | } 194 | 195 | h2 + .gridBlock { 196 | padding-top: 20px; 197 | } 198 | } 199 | 200 | @media only screen and (min-width: 1400px) { 201 | .gridBlock { 202 | display: flex; 203 | flex-direction: row; 204 | flex-wrap: wrap; 205 | } 206 | } 207 | 208 | .alignCenter { 209 | text-align: center; 210 | } 211 | 212 | .alignRight { 213 | text-align: right; 214 | } 215 | 216 | .imageAlignSide { 217 | display: flex; 218 | flex-flow: row wrap; 219 | } 220 | 221 | .blockImage { 222 | max-width: 730px; 223 | } 224 | 225 | .imageAlignSide .blockImage { 226 | flex: 0 1 500px; 227 | max-width: 500px; 228 | } 229 | 230 | @media only screen and (max-width: 735px) { 231 | .imageAlignSide .blockImage { 232 | display: none; 233 | } 234 | } 235 | 236 | .imageAlignSide .blockContent { 237 | flex: 1 1; 238 | } 239 | 240 | .imageAlignBottom .blockImage { 241 | margin: 0 auto 20px; 242 | max-width: 730px; 243 | } 244 | 245 | .imageAlignBottom.alignCenter .blockImage { 246 | margin-left: auto; 247 | margin-right: auto; 248 | } 249 | 250 | .imageAlignTop .blockImage { 251 | margin-bottom: 20px; 252 | max-width: 80px; 253 | } 254 | 255 | .imageAlignTop.alignCenter .blockImage { 256 | margin-left: auto; 257 | margin-right: auto; 258 | } 259 | 260 | .imageAlignRight .blockImage { 261 | margin-left: 40px; 262 | } 263 | 264 | .imageAlignLeft .blockImage { 265 | margin-right: 40px; 266 | } 267 | 268 | .block-container .gridBlock .blockContent p { 269 | padding: 0; 270 | } 271 | 272 | .block-container .wrapper .alignCenter h2 { 273 | text-align: center; 274 | } 275 | 276 | .block-container .wrapper .imageAlignSide h2 { 277 | text-align: left; 278 | } 279 | 280 | .block-container .wrapper .imageAlignSide p { 281 | margin: 0 0 40px; 282 | max-width: 560px; 283 | } 284 | 285 | .block-container { 286 | padding-left: 1rem; 287 | padding-left: var(--ifm-spacing-horizontal); 288 | padding-right: 1rem; 289 | padding-right: var(--ifm-spacing-horizontal); 290 | width: 100%; 291 | } 292 | 293 | .block-container.lightBackground { 294 | background: var(--ifm-light-background-color); 295 | } 296 | 297 | .block-container .wrapper { 298 | margin: 0 auto; 299 | max-width: 1140px; 300 | max-width: var(--ifm-container-width); 301 | } 302 | 303 | .highlightBackground { 304 | background: rgba(153, 66, 79, 0.7); 305 | color: #fff; 306 | } 307 | 308 | .highlightBackground a { 309 | font-weight: 800; 310 | } 311 | 312 | .block-container.paddingAll { 313 | padding: 40px; 314 | } 315 | 316 | .block-container.paddingBottom { 317 | padding-bottom: 80px; 318 | } 319 | 320 | .block-container.paddingLeft { 321 | padding-left: 40px; 322 | } 323 | 324 | .block-container.paddingRight { 325 | padding-right: 40px; 326 | } 327 | 328 | .block-container.paddingTop { 329 | padding-top: 80px; 330 | } 331 | 332 | @media only screen and (max-width: 735px) { 333 | .block-container.paddingBottom { 334 | padding-bottom: 40px; 335 | } 336 | 337 | .block-container.paddingTop { 338 | padding-top: 20px; 339 | } 340 | } 341 | 342 | 343 | body { 344 | font-size: 18px; 345 | } 346 | 347 | 348 | .projectTaglineWrapper { 349 | display: flex; 350 | justify-content: center; 351 | } 352 | 353 | .projectTagline { 354 | max-width: 500px; 355 | flex: 1; 356 | font-size: 115%; 357 | } 358 | 359 | .imageAlignSide .blockImage { 360 | text-align: center; 361 | display: flex; 362 | justify-content: center; 363 | align-items: center; 364 | } 365 | 366 | .logos img, 367 | .showcaseSection .logos img { 368 | width: auto; 369 | max-width: 250px; 370 | } 371 | 372 | details > summary { 373 | cursor: pointer; 374 | } 375 | 376 | @media only screen and (min-width: 1024px) { 377 | .projectTitle { 378 | margin-top: 80px; 379 | } 380 | } 381 | 382 | @media only screen and (min-width: 1200px) { 383 | .homeContainer .homeWrapper .projectLogo img { 384 | max-height: 128px; 385 | max-width: 128px; 386 | height: auto; 387 | } 388 | } 389 | -------------------------------------------------------------------------------- /website/src/pages/index.js: -------------------------------------------------------------------------------- 1 | /** 2 | * Copyright (c) 2017-present, Facebook, Inc. 3 | * 4 | * This source code is licensed under the MIT license found in the 5 | * LICENSE file in the root directory of this source tree. 6 | */ 7 | 8 | import React from 'react' 9 | import useDocusaurusContext from '@docusaurus/useDocusaurusContext' 10 | import { GridBlock } from '../components/GridBlock' 11 | import { Container } from '../components/Container' 12 | import Layout from '@theme/Layout' 13 | 14 | const HomeSplash = (props) => { 15 | const { language = '' } = props 16 | const { siteConfig } = useDocusaurusContext() 17 | const { baseUrl, customFields } = siteConfig 18 | const docsPart = `${customFields.docsPath ? `${customFields.docsPath}/` : ''}` 19 | const langPart = `${language ? `${language}/` : ''}` 20 | const docUrl = (doc) => `${baseUrl}${docsPart}${langPart}${doc}` 21 | 22 | const SplashContainer = (props) => ( 23 |
24 |
25 |
{props.children}
26 |
27 |
28 | ) 29 | 30 | const Logo = (props) => ( 31 |
32 | Project Logo 33 |
34 | ) 35 | 36 | const ProjectTitle = () => ( 37 |
38 |

{siteConfig.title}

39 |
40 |

{siteConfig.tagline}

41 |
42 |
43 | ) 44 | 45 | const Button = (props) => ( 46 | 51 | {props.children} 52 | 53 | ) 54 | 55 | return ( 56 | 57 | 58 |
59 | 60 |
61 | 62 |
63 |
64 | 65 | ) 66 | } 67 | 68 | export default class Index extends React.Component { 69 | render() { 70 | const { config: siteConfig, language = '' } = this.props 71 | const { baseUrl } = siteConfig 72 | 73 | const Block = (props) => ( 74 | 79 | 85 | 86 | ) 87 | 88 | const FeatureCallout = () => ( 89 | 90 |
91 |

92 | 93 | Time series data modeling has broad significance in public health, finance 94 | and engineering 95 | 96 |

97 |
98 | 99 | ) 100 | 101 | const Problem = () => ( 102 | 103 | 104 | {[ 105 | { 106 | title: '', 107 | content: '## Why Time Series? \n - Time series data modeling has broad significance in public health, finance and engineering. \n - Traditional time series methods from statistics often rely on strong modeling assumptions, or are computationally expensive. \n - Given the rise of large-scale sensing data and significant advances in deep learning, the goal of the project is to develop an efficient and user-friendly deep learning library that would benefit the entire research community and beyond.', 108 | image: `${baseUrl}img/time-series-graph.png`, 109 | imageAlt: 'The problem (picture of a question mark)', 110 | imageAlign: 'right', 111 | }, 112 | ]} 113 | 114 | 115 | ) 116 | 117 | const Solution = () => [ 118 | 119 | {[ 120 | { 121 | title: '', 122 | image: `${baseUrl}img/why.png`, 123 | imageAlign: 'left', 124 | imageAlt: 'The solution (picture of a star)', 125 | content: '## Why TorchTS? \n - Existing time series analysis libraries include [statsmodels](https://www.statsmodels.org/stable/index.html), [sktime](https://github.com/alan-turing-institute/sktime). However, these libraries only include traditional statistics tools such as ARMA or ARIMA, which do not have the state-of-the-art forecasting tools based on deep learning. \n - [GluonTS](https://ts.gluon.ai/) is an open-source time series library developed by Amazon AWS, but is based on MXNet. \n - [Pyro](https://pyro.ai/) is a probabilistic programming framework based on PyTorch, but is not focused on time series forecasting.', 126 | }, 127 | ]} 128 | , 129 | ] 130 | 131 | const Features = () => ( 132 | 133 | {[ 134 | { 135 | content: 'Library built on pytorch', 136 | image: `${baseUrl}img/pytorch-logo.png`, 137 | imageAlign: 'top', 138 | title: 'Built On Pytorch', 139 | }, 140 | { 141 | content: 'Easy to use with model.predict', 142 | image: `${baseUrl}img/puzzle.png`, 143 | imageAlign: 'top', 144 | title: 'User Friendly', 145 | }, 146 | { 147 | content: 'Easily Scalable Library', 148 | image: `${baseUrl}img/scalable.png`, 149 | imageAlign: 'top', 150 | title: 'Scalable', 151 | }, 152 | ]} 153 | 154 | ) 155 | 156 | return ( 157 | 158 | 159 |
160 | 161 | 162 | 163 | 164 |
165 | 166 | ) 167 | } 168 | } 169 | -------------------------------------------------------------------------------- /website/src/pages/index.module.css: -------------------------------------------------------------------------------- 1 | /* stylelint-disable docusaurus/copyright-header */ 2 | 3 | /** 4 | * CSS files with the .module.css suffix will be treated as CSS modules 5 | * and scoped locally. 6 | */ 7 | 8 | .heroBanner { 9 | padding: 4rem 0; 10 | text-align: center; 11 | position: relative; 12 | overflow: hidden; 13 | } 14 | 15 | @media screen and (max-width: 966px) { 16 | .heroBanner { 17 | padding: 2rem; 18 | } 19 | } 20 | 21 | .buttons { 22 | display: flex; 23 | align-items: center; 24 | justify-content: center; 25 | } 26 | -------------------------------------------------------------------------------- /website/static/.nojekyll: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/website/static/.nojekyll -------------------------------------------------------------------------------- /website/static/img/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/website/static/img/logo.png -------------------------------------------------------------------------------- /website/static/img/logo2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/website/static/img/logo2.png -------------------------------------------------------------------------------- /website/static/img/puzzle.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/website/static/img/puzzle.png -------------------------------------------------------------------------------- /website/static/img/pytorch-logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/website/static/img/pytorch-logo.png -------------------------------------------------------------------------------- /website/static/img/scalable.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/website/static/img/scalable.png -------------------------------------------------------------------------------- /website/static/img/time-series-graph.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/website/static/img/time-series-graph.png -------------------------------------------------------------------------------- /website/static/img/torchTS_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/website/static/img/torchTS_logo.png -------------------------------------------------------------------------------- /website/static/img/why.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rose-STL-Lab/torchTS/93597cac5363a1377342146682d6af998cfc9734/website/static/img/why.png --------------------------------------------------------------------------------