Final answer:
The suitable cross-validation technique for a time series dataset is Time Series Cross-Validation, with one popular approach being the rolling window cross-validation.
Step-by-step explanation:
When working with a time series dataset, a suitable cross-validation technique is Time Series Cross-Validation. This technique takes into account the temporal nature of the data and ensures that the model is evaluated in a realistic manner. One popular approach is the rolling window cross-validation, where the dataset is split into training and testing sets by sliding a fixed-size window across the series.
For example, if we have 100 time points, we can start with the first 70 as the training set and the next 30 as the testing set. Then, we slide the window by one time point and repeat the process. This allows the model to be evaluated on different periods of the time series.
The idea is to simulate real-world forecasting scenarios where the model is trained on past data and tested on future data.