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LSTNet- Iterated Forecasting

Note: This repository is a followup of the implementation of fbadine/LSTNet The Iterated forecasting method is described in this paper. This approach is applied here to real-time stocks. The default arguments of the LSTNet model have been changed a bit, you can see them in the run_forecast.py file

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks paper

HOW TO USE

from run_forecast import FetchStocks, LSTNetModel, LSTNetIteratedModel
fetcher=FetchStocks()
fetcher.df

Output: Dataframe of stocks..

## Creating an instance of the class LSTNetIterated
saved_csv_path=fetcher.csv_path
lstnet_iterated=LSTNetIteratedModel(n_series=48,forecast_steps=15, csv_path=saved_csv_path)

lstnet_iterated.train_model()

training...
Epoch 1/100

 1/38 [..............................] - ETA: 3:14 - loss: 0.5679 - rse: 3.1090 - corr: 0.4719
10/38 [======>.......................] - ETA: 0s - loss: 0.3846 - rse: 2.4926 - corr: 0.2892  
20/38 [==============>...............] - ETA: 0s - loss: 0.2997 - rse: 2.0112 - corr: 0.3071
29/38 [=====================>........] - ETA: 0s - loss: 0.2537 - rse: 1.7078 - corr: 0.3361
38/38 [==============================] - ETA: 0s - loss: 0.2246 - rse: 1.4998 - corr: nan   
38/38 [==============================] - 6s 31ms/step - loss: 0.2246 - rse: 1.4998 - corr: nan - val_loss: 0.1273 - val_rse: 1.0666 - val_corr: 0.4246
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Epoch 99/100

 1/38 [..............................] - ETA: 0s - loss: 0.0115 - rse: 0.0863 - corr: 0.9857
10/38 [======>.......................] - ETA: 0s - loss: 0.0120 - rse: 0.0912 - corr: 0.9842
20/38 [==============>...............] - ETA: 0s - loss: 0.0116 - rse: 0.0876 - corr: 0.9846
29/38 [=====================>........] - ETA: 0s - loss: 0.0115 - rse: 0.0871 - corr: 0.9846
38/38 [==============================] - 0s 8ms/step - loss: 0.0114 - rse: 0.0860 - corr: nan - val_loss: 0.0144 - val_rse: 0.1366 - val_corr: 0.5741
Epoch 100/100

 1/38 [..............................] - ETA: 0s - loss: 0.0123 - rse: 0.0929 - corr: 0.9861
10/38 [======>.......................] - ETA: 0s - loss: 0.0112 - rse: 0.0848 - corr: 0.9870
19/38 [==============>...............] - ETA: 0s - loss: 0.0110 - rse: 0.0843 - corr: 0.9873
29/38 [=====================>........] - ETA: 0s - loss: 0.0110 - rse: 0.0835 - corr: 0.9868
38/38 [==============================] - 0s 8ms/step - loss: 0.0112 - rse: 0.0850 - corr: nan - val_loss: 0.0144 - val_rse: 0.1360 - val_corr: 0.5748
training now...
Figure(1600x1000)
model saved...

model saved
Outputs can be found at: LSTNet/save/large_portfolio_horizon1_window7_skip7

Output: Forecast..

forecast=lstnet_iterated.get_forecast()

lstnet_iterated.plot_lstnet_forecast(series_index=24)