A Practical Approach to Timeseries Forecasting Using Python - LSTM Parameter Change and Stacked LSTM

The video tutorial explores the impact of changing the number of epochs in a deep learning model, specifically focusing on the effects of overfitting when using 500 epochs. It demonstrates how overfitting can lead to inaccurate forecasting results, characterized by unexpected peaks. The tutorial then transitions to adding two LSTM layers, comparing the performance of single versus double LSTM configurations. It concludes that a single LSTM is more effective for the given dataset size, while larger datasets may benefit from more complex architectures. The video ends with a preview of the next topic, Bi LSTM.

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