├── .DS_Store ├── Chapter 01 ├── Icon ├── _aux │ ├── Icon │ ├── anomaly.py │ ├── climate.py │ ├── forecast.py │ ├── heart_beats.py │ ├── london_temperature.py │ ├── modeling.py │ └── tesla_stock.py ├── classical_ar.py ├── classical_arima.py ├── classical_hwes.py ├── classical_sarima.py ├── random_walk.py ├── seasonality.py ├── trend_linear.py └── trend_nonlinear.py ├── Chapter 02 ├── Icon ├── dfdx │ ├── Icon │ ├── backward.py │ ├── computational_graph.py │ ├── derivatives.py │ ├── function.py │ └── gradient.py ├── layers │ ├── Icon │ ├── activation_relu.py │ ├── activation_sigmoid.py │ ├── activation_tanh.py │ ├── avg_pool_2d.py │ ├── conv1d.py │ ├── conv1d_padding.py │ ├── conv1d_stride.py │ ├── conv2d.py │ ├── dropout.py │ ├── linear.py │ └── max_pool_2d.py ├── nn │ ├── Icon │ ├── dataset.py │ ├── dummy_model.py │ ├── example.py │ ├── fcnn_model.py │ ├── hwes_model.py │ ├── linear_interpolation_model.py │ └── time_series.py └── nn_architecture │ ├── Icon │ ├── mnist.py │ ├── named_children.py │ └── train_eval.py ├── Chapter 03 ├── Icon ├── dataset │ ├── Icon │ ├── analyze.py │ └── sliding_window.py ├── preprocessing │ ├── Icon │ ├── detrend.py │ ├── differencing.py │ └── normalize.py └── uk_temperature_prediction │ ├── Icon │ ├── data │ ├── Icon │ └── MET_Office_Weather_Data.csv │ ├── example.py │ ├── interpolated_time_series.py │ ├── model │ ├── Icon │ ├── dl_model.py │ ├── hwes_model.py │ └── sarima_model.py │ ├── optimization.py │ ├── raw_time_series.py │ └── training_datasets.py ├── Chapter 04 ├── Icon ├── aep_hourly_timeseries.py ├── data │ ├── AEP_hourly.csv │ ├── Icon │ ├── NI_hourly.csv │ └── PJME_hourly.csv ├── gru_example.py ├── lstm_example.py ├── model │ ├── Icon │ ├── gru.py │ ├── lstm.py │ └── rnn.py ├── ni_hourly_timeseries.py ├── pjme_hourly_timeseries.py ├── rnn_example.py └── training_datasets.py ├── Chapter 05 ├── Icon ├── enc_dec │ ├── Icon │ ├── example.py │ ├── model.py │ └── ts.py ├── tcn │ ├── Icon │ ├── casual_convolution.py │ ├── dummy.py │ ├── example.py │ ├── model.py │ └── ts.py └── training_datasets.py ├── Chapter 06 ├── Icon ├── hopt │ ├── Icon │ ├── gru │ │ ├── Icon │ │ ├── data │ │ │ ├── Icon │ │ │ └── PJME_hourly.csv │ │ ├── model │ │ │ ├── Icon │ │ │ └── gru.py │ │ ├── search.py │ │ ├── training_datasets.py │ │ └── trial.py │ ├── hello_world │ │ ├── Icon │ │ ├── search.py │ │ └── trial.py │ └── nested_ss.json ├── hybrid │ ├── Icon │ ├── data │ │ ├── AEP_hourly.csv │ │ └── Icon │ ├── filters.py │ ├── model.py │ ├── search.py │ ├── training_datasets.py │ └── trial.py └── nas │ ├── Icon │ ├── model.py │ ├── nas_to_hopt.py │ ├── search.py │ ├── training_datasets.py │ ├── trial.py │ └── ts.py ├── Chapter 07 ├── Icon ├── covid │ ├── Icon │ ├── analyze.py │ ├── data │ │ ├── COVID_19_complete.csv │ │ ├── COVID_19_until_2021_02_01.csv │ │ ├── Icon │ │ ├── README.md │ │ └── best_model_bk.pth │ ├── dataset.py │ ├── evaluate.py │ ├── model │ │ ├── Icon │ │ └── model.py │ ├── prediction.py │ ├── search.py │ ├── train.py │ ├── train_and_save.py │ ├── trial.py │ └── utils.py ├── stock │ ├── Icon │ ├── data │ │ ├── Icon │ │ ├── MSFT_until_2020_01_01.csv │ │ ├── MSFT_until_2021_01_01.csv │ │ ├── best_model.pth │ │ └── best_model_bk.pth │ ├── dataset.py │ ├── download.py │ ├── evaluate.py │ ├── indicators.py │ ├── model │ │ ├── Icon │ │ └── model.py │ ├── search.py │ ├── train.py │ ├── train_and_save.py │ ├── trial.py │ ├── ts_diff.py │ ├── utils.py │ └── visualize.py └── weather │ ├── Icon │ ├── data │ ├── Icon │ ├── README.md │ ├── best_model_bk.pth │ ├── weatherAUS_complete.csv │ └── weatherAUS_until_2016_01_01.csv │ ├── dataset.py │ ├── evaluate.py │ ├── model │ ├── Icon │ └── model.py │ ├── search.py │ ├── train.py │ ├── train_and_save.py │ ├── trial.py │ ├── utils.py │ └── visualize.py ├── Chapter 08 ├── Icon ├── custom_model.py ├── data │ ├── Icon │ └── MET_Office_Weather_Data.csv ├── dataset.py ├── deepar_model.py ├── example.py └── forecasting_model.py ├── LICENSE └── README.md 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