A Practical Approach to Timeseries Forecasting Using Python - BiLSTM for Time Series Forecasting

The video tutorial discusses configuring LSTM and bidirectional LSTM models, focusing on adjusting epochs and commenting on specific parts of the model. It highlights the implications of using bidirectional LSTM, especially with small datasets, and analyzes the results of different configurations. The tutorial also covers layer adjustments and the impact on overfitting, concluding with the final model setup and a summary of key points.

What changes were made to the epochs in the model?

Explain the significance of using bidirectional LSTM in the model.

What was the result of using a single bi LSTM for 62 epochs?

Discuss the impact of increasing the number of epochs on overfitting.

What adjustments were suggested for the activation function in the bidirectional LSTM?

How does the performance of stacked LSTM compare to single LSTM in the context of small datasets?

What are the key factors to consider when choosing between LSTM and bi-directional LSTM?