What cross-validation technique would you use on a time series dataset?

Shriyansh

I am learning machine learning and was curious about which cross-validation technique would you use on a time series dataset.

ankit_roy_07

When working with a time series dataset, traditional cross-validation techniques like k-fold cross-validation may not be appropriate because they assume that the data points are independent and identically distributed (i.i.d.), which is often not the case in time series data. Time series data have temporal dependencies, where the order and timing of data points matter.

To address this issue, you should use time series-specific cross-validation techniques that respect the temporal structure of the data. Two common time series cross-validation methods are:

1. Time Series Cross-Validation (TimeSeriesSplit): This technique is an extension of k-fold cross-validation specifically designed for time series data. Instead of randomly splitting the data, it splits the data in a time-ordered manner. Each fold contains a contiguous block of data, ensuring that past data is used to predict future data. The number of folds is typically determined by the user.

For example, if you have a time series dataset with 100 data points and decide to use 5-fold time series cross-validation, the folds might look like this:

Fold 1: Train on data points 1-20, Test on data points 21-40
Fold 2: Train on data points 1-40, Test on data points 41-60
Fold 3: Train on data points 1-60, Test on data points 61-80
Fold 4: Train on data points 1-80, Test on data points 81-100
Fold 5: Train on data points 1-100, Test on data points 1-20

2. Walk-forward Validation: This is a simpler but effective technique for time series cross-validation. It involves training the model on historical data up to a certain point and then forecasting the next data point. The process is repeated iteratively, with the training window sliding forward one step at a time.

For example, suppose you have a time series dataset with 100 data points. You start by training the model on data points 1-80 and then forecast data point 81. Next, you train on data points 1-81 forecast data point 82, and so on until you reach the end of the dataset.

The choice between Time Series Cross-Validation and forward validation depends on the specific requirements of your time series forecasting problem. In both cases, it's crucial to maintain the temporal order of the data to simulate the real-world scenario where you make predictions based on historical information. These techniques help you assess the generalization performance of your model and identify whether it can make accurate predictions on future, unseen data points.