Random Forest for Time Series Forecasting
Random Forest is a popular and effective ensemble machine learning algorithm.
It is widely used for classification and regression predictive modeling problems with structured (tabular) data sets, e.g. data as it looks in a spreadsheet or database table.
Random Forest can also be used for time series forecasting, although it requires that the time series dataset be transformed into a supervised learning problem first. It also requires the use of a specialized technique for evaluating the model called walk-forward validation, as evaluating the model using k-fold cross validation would result in optimistically biased results.
In this tutorial, you will discover how to develop a Random Forest model for time series forecasting.
After completing this tutorial, you will know: