A Practical Approach to Timeseries Forecasting Using Python - Dataset Reshaping

This video tutorial covers the process of preparing data for LSTM models. It begins with setting up empty arrays for training and testing, defining the number of future and past days for prediction, and reshaping the input data into the required dimensions. The tutorial explains how to calculate the range for data shaping, append values to training arrays, and convert these arrays into the correct format. Finally, it demonstrates how to print the shapes of the training data and concludes with a brief overview of the next steps in the series.

What values are appended to train X during the training process?

Evaluate responses using AI:

OFF

Describe the shape of train Y and how it is derived.

Evaluate responses using AI:

OFF

What is the final shape of train X and train Y as mentioned in the text?

Evaluate responses using AI:

OFF