Data Science - Time Series Forecasting with Facebook Prophet in Python - Walk-Forward Validation

The video tutorial introduces walk forward validation as a suitable method for time series data, contrasting it with traditional train-test splits and K-fold cross validation, which are inadequate due to time dependencies. Walk forward validation involves incrementally training models with past data to predict future outcomes, reflecting real-world scenarios. The tutorial also discusses practical considerations, such as step sizes and data windowing, and highlights limitations of using Psykitlearn for time series validation.

Discuss the implications of using future data in model training.

What is walk forward validation and how does it differ from traditional cross validation?

What considerations should be made regarding the size of the training set in time series analysis?