Python by Examples: Target Transformations for Time Series Forecasting

In time series forecasting, transformations of the target variable are key to achieving accurate and generalizable models. Many time series display non-stationary behaviors that can challenge traditional modeling techniques.

A variety of techniques exist to handle issues such as non-stationarity, unit roots, and seasonality. By applying differencing, statistical tests, and decomposition methods, data scientists can transform raw time series into stationary series that are more amenable to forecasting. In this article, we illustrate practical examples using Python to detect and correct these issues while ensuring all samples are immediately runnable. The examples aim to provide clarity on transforming target variables and pave the way for improved model performance.

Handling Non-Stationarity in Time Series

Many forecasting models require the underlying time series to exhibit stationarity — constant properties like the mean and variance over time. In this section, we simulate a non-stationary time series and demonstrate how differencing can help stabilize the series. We also use statistical tests to assess stationarity, ensuring that our manipulations lead to improved patterns for forecasting.