├── .gitignore ├── 01-Create-Datasets ├── 01-create-retail-datasets.ipynb ├── 02-create-online-retail-II-datasets.ipynb ├── 03-create-air-quality-dataset.ipynb ├── 04-create-air-passengers-dataset.ipynb └── 05-create-electricity-demand-dataset.ipynb ├── 02-Tabularizing-Time-Series ├── 01-data-analysis-air-pollutants.ipynb ├── 02-feature-engineering-air-pollutants.ipynb └── 03-forecasting-air-pollutants.ipynb ├── 03-Challenges-in-Time-Series-Forecasting ├── 01-Refactoring-feature-engineering.ipynb ├── 02-forecasting-one-step-ahead.ipynb ├── 03-multistep-forecasting-direct.ipynb ├── 04-multistep-forecasting-recursive.ipynb └── 05-multistep-forecasting-recursive-continued.ipynb ├── 04-Time-Series-Decomposition ├── 01-box-cox-transform.ipynb ├── 02-compute-moving-averages.ipynb ├── 03-classical-decomposition-to-compute-trend-and-seasonality.ipynb ├── 04-LOWESS-to-compute-trend.ipynb ├── 05-STL-to-compute-trend-and-seasonality.ipynb └── 06-MSTL-decomposition.ipynb ├── 05-Missing-Data ├── 01-impute-missing-data-using-forward-fill-backward-fill.ipynb ├── 02-impute-missing-data-using-linear-and-spline-interpolation.ipynb └── 03-impute-missing-data-using-STL-decomposition-and-interpolation.ipynb ├── 06-Outliers ├── 01-detect-outliers-using-rolling-statistics.ipynb ├── 02-detect-outliers-using-residuals-LOWESS.ipynb ├── 03-detect-outliers-using-residuals-STL.ipynb └── 04-modelling-outliers-with-dummy-variables.ipynb ├── 07-Lag-Features ├── 01-computing-lags.ipynb ├── 02-lag-plots.ipynb ├── 03-autocorrelation-function.ipynb ├── 04-partial-autocorrelation-function.ipynb ├── 05-cross-correlation-function.ipynb ├── 06-air-pollution-example-domain-knowledge.ipynb ├── 07-air-pollution-example-modelling.ipynb └── 08-air-pollution-example-correlation.ipynb ├── 08-Window-Features ├── 01-rolling-window-features.ipynb ├── 02-expanding-window-features.ipynb ├── 03-weighted-rolling-window-features.ipynb ├── 04-exponential-weights.ipynb └── 05-window-features-with-feature-selection.ipynb ├── 09-Trend-Features ├── 01-time-linear-trend.ipynb ├── 02-time-non-linear-trend.ipynb ├── 03-recursive-forecasting-example.ipynb ├── 04-piecewise-linear-trend-and-changepoints.ipynb ├── 05-tree-based-models-and-trend.ipynb ├── 06-linear-trees-lightgbm.ipynb └── images │ ├── forecast_with_just_time.png │ └── recursive_forecasting │ ├── Slide1.png │ ├── Slide2.png │ ├── Slide3.png │ └── Slide4.png ├── 10-Seasonality-Features ├── 01-seasonal-lags.ipynb ├── 02-datetime-features-seasonality.ipynb ├── 03-seasonal-dummies.ipynb └── 04-fourier-features.ipynb ├── 11-Time-Features ├── 01-Extracting-date-related-features.ipynb ├── 02-Extracting-time-related-features.ipynb ├── 03-datetime-with-Feature-engine.ipynb ├── 04-periodic-features.ipynb ├── 05-highlighting-holidays-sandbox.ipynb └── 05-highlighting-holidays.ipynb ├── 12-Categorical-Encoding ├── 1-one-hot-encoding.ipynb ├── 2-ordinal-encoding.ipynb ├── 3-mean-encoding-simple.ipynb └── 4-mean-encoding-expanding-window.ipynb ├── Appendix └── 00-pandas-period.ipynb ├── Datasets └── .gitkeep ├── LICENSE ├── README.md ├── assignments └── 02-tabularizing-time-series │ ├── assignment.ipynb │ └── solution.ipynb ├── images ├── FETSF_banner.png ├── forecasting_framework.png ├── lag_features.png ├── trainindata.png └── window_features.png └── requirements.txt /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/trainindata/feature-engineering-for-time-series-forecasting/HEAD/.gitignore -------------------------------------------------------------------------------- /01-Create-Datasets/01-create-retail-datasets.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/trainindata/feature-engineering-for-time-series-forecasting/HEAD/01-Create-Datasets/01-create-retail-datasets.ipynb -------------------------------------------------------------------------------- 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