time series forecasting using pytorch，including ANN,RNN,LSTM,GRU and TSR-RNN，experimental code
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time_series_forecasting_pytorch
Experimental source code: Time series forecasting using pytorch，including MLP,RNN,LSTM,GRU, ARIMA, SVR, RF and TSR-RNN models.
Requirements
- python 3.6.3 (Anaconda)
- keras 2.1.2
- PyTorch 1.0.1
- tensorflow-gpu 1.13.1
- sklearn 0.19.1
- numpy 1.15.4
- pandas 0.23.4
- statsmodels 0.9.0
- matplotlib 2.1.0
Code
- ARIMA.py: ARIMA model, iteration version
- Holt_Winters.py Holt-Winters model, only primary version
- eval.py: evaluation metrics, including RMSE,MAE,MAPE and SMAPE.
- NN_forecasting.py:neural networks forecasting
- model.py: neural network models
- train.py: training and predicting of neural network models, including RNN, LSTM, GRU, MLP, TSR-RNN
- ts_decompose.py: time series decomposition
- ts_loader: data loader for neural network models
- ML_forecasting.py: general machine learning models, including SVR and RF
- util.py: data loader