A Practical Approach to Timeseries Forecasting Using Python - Data Splitting

What is the primary purpose of splitting data into training and testing sets in time series forecasting?

• To eliminate outliers from the dataset

• To separate data for model training and evaluation

• To improve the accuracy of predictions

• To reduce the size of the dataset

What is the role of the split date in the data splitting process?

• It is used to merge different datasets

• It is used to divide the dataset into training and testing parts

• It determines the size of the dataset

• It helps in identifying outliers

Which of the following is NOT a step in computing the training and testing datasets?

• Using the split date to divide the data

• Calculating the mean of the dataset

• Assigning data before the split date to training

• Creating empty dictionaries

How can you verify the amount of data in the training and testing datasets?

• By checking the data types

• By printing the length of each dataset

• By calculating the average values

• By visualizing the data

Why is it important to perform data splitting before time series forecasting?

• To ensure the model is trained on future data

• To allow for testing the model's performance

• To increase the dataset size

• To simplify the data processing