In recent years, the use of machine learning (ML) models has become popular and accessible due to significant improvement in standard computation power. This led to a new world of methods and approaches for regression and classifications models. The process of creating time series forecasting with ML models follows the same process we used in Chapter 9, Forecasting with Linear Regression, with the linear regression model.
Before we start diving into the details, it is important to caveat the use of ML models in the context of time series forecasting:
- Cost: The use of ML models is typically more expensive than typical regression models, both in computing power and time.
- Accuracy: The ML model's performance is highly dependent on the quality (that is, strong casualty relationship with the dependent variable) of the predictors....
