When working with time series data, it's important to use a cross-validation technique that respects the temporal order of the observations. The most commonly used cross-validation technique for time series data is the Rolling Origin Cross-Validation, also known as Time Series Cross-Validation or Walk-Forward Validation.
Here's how Rolling Origin Cross-Validation works:
Define a fixed-size rolling window (or time horizon) for training and a subsequent fixed-size window for validation.
Start by splitting the time series data into an initial training set and a validation set based on the defined window sizes.
Train the model on the initial training set and evaluate its performance on the corresponding validation set.
Move the rolling window forward by a specified step size. The new training set will include the data from the previous training set plus the data from the previous validation set, while the new validation set will consist of the subsequent observations.
Repeat steps 3 and 4 until the end of the time series is reached.
Calculate the overall performance metric by averaging the metrics obtained from each validation set.
The key advantages of using Rolling Origin Cross-Validation for time series data are:
- It maintains the temporal order of the observations, ensuring that the model is always trained on data from the past and evaluated on future data.
- It allows for multiple evaluations of the model's performance over different time periods, providing a more robust assessment of the model's generalization ability.
- It can help in detecting concept drift or changes in the underlying patterns of the time series over time.
However, it's important to note that the choice of the window sizes for training and validation, as well as the step size for moving the rolling window, can impact the results. These hyperparameters should be carefully selected based on the characteristics of the time series data and the specific problem at hand.
Additionally, in some cases, other variations of cross-validation techniques, such as Nested Cross-Validation or Time Series Split, may be used depending on the specific requirements of the time series problem and the available data.