4 Common Machine Learning Data Transforms for Time Series Forecasting

Last Updated on August 28, 2019

Time series data often requires some preparation prior to being modeled with machine learning algorithms.

For example, differencing operations can be used to remove trend and seasonal structure from the sequence in order to simplify the prediction problem. Some algorithms, such as neural networks, prefer data to be standardized and/or normalized prior to modeling.

Any transform operations applied to the series also require a similar inverse transform to be applied on the predictions. This is required so that the resulting calculated performance measures are in the same scale as the output variable and can be compared to classical forecasting methods.

In this post, you will discover how to perform and invert four common data transforms for time series data in machine learning.

After reading this post, you will know:

• How to transform and inverse the transform for four methods in Python.
• Important considerations when using transforms on training and test datasets.
• The suggested order for transforms when multiple operations are required on a dataset.

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