A Practical Approach to Timeseries Forecasting Using Python - Dataset Division

The video tutorial explains how to create and manipulate two lists, train X and train Y, for a machine learning model. It covers setting future and past values, defining a range for these lists, and appending values to them. The tutorial also demonstrates converting these lists into arrays and understanding their shapes. Finally, it introduces the implementation of an LSTM model.

What are the two lists created at the beginning of the process?

• train M and train N

• train X and train Y

• train P and train Q

• train A and train B

What is the value of north_future in the tutorial?

• 10

• 1

• 5

• 25

What is the starting point for the range of train X and train Y?

• north_future

• N_past

• DF length

• train Y length

How many values are appended to train X in each iteration?

• 20

• 25

• 15

• 10

What is the shape of the training data used to determine the next column in train X?

• DF for training_shape one

• DF for training_shape four

• DF for training_shape three

• DF for training_shape two

How many values are appended to train Y in each iteration?

• 1

• 5

• 25

• 10

What is the next step after preparing train X and train Y arrays?

• Implementing SVM

• Implementing LSTM

• Implementing CNN

• Implementing RNN