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The sole purpose for the development of this software was to make it easy for learners to understand how tuning several hyperparameters can effect the result of an LSTM (Long Short Term Memory) network on various types of time series data.

• Python 3.x
• Tensorflow
• Matplotlib

• Install REQUIREMENTS.txt, by running pip install -r REQUIREMENTS.txt
• Open app.py file
• Set the hyperparameter values ( Dropout, Lag, Test Ratio, Max Epoch )
• Select a preloaded dataset
• Click Start
• To reset the console click Reset

The application has been tested on Windows and Linux platforms. In case of any issue, feel free to raise an issue.

Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= unified_lstm (UnifiedLSTM) (None, LAG-1, 30) 3840 _________________________________________________________________ dropout (Dropout) (None, LAG-1, 30) 0 _________________________________________________________________ unified_lstm_1 (UnifiedLSTM) (None, 30) 7320 _________________________________________________________________ dense (Dense) (None, 1) 31 ================================================================= Total params: 11,191 Trainable params: 11,191 Non-trainable params: 0 _________________________________________________________________ None 

Currently, the application supports 5 different datasets. We are going to add more datasets and probably improve the model in the next iteration of development. Contributions are welcomed.

• Sine Wave
• Cosine Wave
• Increasing Sales
• Decreasing Sales
• Random Data

Amitrajit Bose + Anirban Mukherjee