Deep Learning - Recurrent Neural Networks with TensorFlow - Autoregressive Linear Model for Time Series Prediction

The video tutorial covers the implementation of an autoregressive linear model for time series prediction. It begins with creating a synthetic sine wave dataset, followed by constructing the dataset for prediction. The tutorial then guides through building and training the model, highlighting the importance of correct data processing. It demonstrates both incorrect and correct forecasting methods, emphasizing the significance of using the right approach. Finally, it explores forecasting with noisy data, showing how the model adapts to noise while maintaining periodicity.

Why is it important to not split the data randomly when training time series models?

What steps are taken to update the variable last X with the latest forecasted prediction?

How does the model handle noise in the time series data?

What does the model predict when using the correct forecasting method?