├── .gitignore ├── Chapter_1 ├── 1.10_resampling_mv.py ├── 1.11_correlation.py ├── 1.1_loading_timeseries.py ├── 1.2_visualizing_timeseries.py ├── 1.3_irregular_resampling.py ├── 1.3_resampling.py ├── 1.4_missing_values.py ├── 1.5_decomposition.py ├── 1.6_autocorrelation.py ├── 1.7_stationarity.py ├── 1.8_variance.py └── 1.9_multivariate_timeseries.py ├── Chapter_2 ├── 2.5_building_simple_nn_pytorch.py ├── 2.6_training_feed_forward_neural_network.py ├── 2.7_training_a_recurrent_neural_network.py ├── 2.8_training_an_LSTM_neural_network.py └── 2.9_training_cnn.py ├── Chapter_3 ├── 3.10_handling_trend.py ├── 3.11_seasonality_dummies_and_fourier.py ├── 3.12_seasonality_differencing.py ├── 3.13_seasonality_decomposition.py ├── 3.14_handling_nonconstant_var.py ├── 3.1_simple_forecasting_models.py ├── 3.2_univariate_forecasting_ARIMA.py ├── 3.3_preparing_time_series_supervised_learning.py ├── 3.4_univariate_forecasting_feedforward_nn.py ├── 3.5_univariate_forecasting_LSTM_nn.py ├── 3.6_univariate_forecasting_GRU_nn.py ├── 3.7_univariate_forecasting_stacking_LSTM.py ├── 3.8_univariate_forecasting_combining_LSTM_multiple_fully_connected_layers.py └── 3.9_univariate_forecasting_CNN.py ├── Chapter_4 ├── 4.1_preparing_mv_timeseries.py ├── 4.2_linear_regression.py ├── 4.3_fnn_for_mv_tseries.py ├── 4.4_lstm_for_mv_tseries.py ├── 4.5_evaluating_dl_forecasting.py ├── 4.6_tensorboard.py └── 4.7_callbacks_es.py ├── Chapter_5 ├── 5.1_multistep_forecasting.py ├── 5.2_multistep_multioutput.py ├── 5.3_understanding_gfm.py ├── 5.4_data_preparation_gfm.py ├── 5.5_global_lstm.py ├── 5.6_global_seasonal.py └── 5.7_hyperparameter_tuning.py ├── Chapter_6 ├── 6.1_training_nbeats.py ├── 6.2_nbeats_learning_rate.py ├── 6.3_getting_started_gluonts.py ├── 6.4_training_deepar.py ├── 6.5_transformer.py ├── 6.6_training_tft.py ├── 6.7_informer_with_nixtla.py └── 6.8_cv_t_with_nixtla.py ├── Chapter_7 ├── 7.1_exceedance_prob.py ├── 7.2_exceedance_w_lstm.py ├── 7.3_conformal_prediction.py ├── 7.4_probabilistic_forecasting_LSTM.py ├── 7.5_deepar_probforecasting.py ├── 7.6_gaussian_processes.py └── 7.7_prophet.py ├── Chapter_8 ├── 8.1_1nn_tsc.py ├── 8.2_tsc_datamodule.py ├── 8.3_cnn_tsc.py ├── 8.4_resnet_tsc.py └── 8.5_tsc_sktime.py ├── Chapter_9 ├── 9.1_arima.py ├── 9.2_neural_prediction.py ├── 9.3_lstm_rec.py ├── 9.4_pyod_autoencoder.py ├── 9.5_pyod_vae.py └── 9.6_pyod_gan.py ├── LICENSE ├── README.md ├── assets ├── daily_multivariate_timeseries.csv └── datasets │ ├── Car │ ├── Car_TEST.tsv │ ├── Car_TRAIN.tsv │ └── README.md │ ├── taxi │ ├── readme.md │ ├── taxi_data.csv │ └── taxi_labels.csv │ ├── time_series_smf1.csv │ └── time_series_solar.csv ├── requirements.txt └── tests └── test_all_scripts.py /.gitignore: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Deep-Learning-for-Time-Series-Data-Cookbook/HEAD/.gitignore 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