Hi Ranjith,
I understand you’re seeing a mismatch at the initial timestep when using your trained LSTM for prediction, but the model aligns well with actual test data after that.
You can try the following recommendations to improve the initial prediction:
- State Initialization: Before making predictions on new data, try updating the LSTM’s state using a short “warm-up” sequence from your test data. In MATLAB, you can use the predictAndUpdateState function with a few initial timesteps before starting your actual prediction.
- Try Including Time as a Feature: If there are trends or seasonality, adding a time or sequence index as an input feature can help the model with initial predictions.
- Expand Training Data: Include more varied starting points or sequences in your training set so the model can generalize better to unseen initial conditions.
You can refer to the following documentation to know more about "predictAndUpdateState" function:
Hope this helps!
