Tutorial: Random Forest for Time Series

 Am a long  time proponent of ensemble methods.  Here Jason Brownlee provides a nice tutorial on an often powerful method everyone should know.  As usual, well done, minimal tech.

Random Forest for Time Series Forecasting   by Jason Brownlee

by Jason Brownlee  in Time Series

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:

Random Forest is an ensemble of decision trees algorithms that can be used for classification and regression predictive modeling.  Time series datasets can be transformed into supervised learning using a sliding-window representation.How to fit, evaluate, and make predictions with an Random Forest regression model for time series forecasting.

Let’s get started.   ....