How To Backtest Machine Learning Models for Time Series Forecasting
Last Updated on August 28, 2019
k-fold Cross Validation Does Not Work For Time Series Data and
Techniques That You Can Use Instead.
The goal of time series forecasting is to make accurate predictions about the future.
The fast and powerful methods that we rely on in machine learning, such as using train-test splits and k-fold cross validation, do not work in the case of time series data. This is because they ignore the temporal components inherent in the problem.
In this tutorial, you will discover how to evaluate machine learning models on time series data with Python. In the field of time series forecasting, this is called backtesting or hindcasting.
After completing this tutorial, you will know:
- The limitations of traditional methods of model evaluation from machine learning and why evaluating models on out of sample data is required.
- How to create train-test splits and multiple train-test splits of time series data for model evaluation in Python.
- How walk-forward validation provides the most realistic evaluation of machine learning models on time series data.
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