ZeitreihenKreuzvalidierung

ZeitreihenKreuzvalidierung, also known as time series cross-validation, is a statistical method used to evaluate the performance of predictive models on time series data. Unlike traditional cross-validation techniques, which randomly shuffle the data, time series cross-validation respects the temporal order of the data. This is crucial because time series data often exhibit trends, seasonality, and autocorrelation, which can be violated if the data is shuffled.

In time series cross-validation, the data is divided into multiple segments, typically called folds. Each fold consists of a training set and a test set, where the test set is always a subsequent time period after the training set. This ensures that the model is evaluated on data it has not seen before, maintaining the temporal integrity of the series.

There are several common approaches to time series cross-validation:

1. Rolling Forecast: In this method, the model is trained on an initial segment of the data and then used to predict the next time period. This process is repeated, each time including the most recent prediction in the training set and using it to predict the next time period.

2. Expanding Window Forecast: Similar to the rolling forecast, but instead of using a fixed-size training window, the training set expands with each iteration, incorporating all previous data points.

3. Fixed Window Forecast: This approach uses a fixed-size training window and a fixed-size test window, sliding both windows across the time series.

The choice of cross-validation method depends on the specific characteristics of the time series and the goals of the analysis. Regardless of the method chosen, time series cross-validation provides a more realistic and reliable assessment of a model's performance on time series data.