Final answer:
When working with time series data, it is not appropriate to use k-fold cross validation or LOOCV. Instead, a commonly used cross validation technique for time series data is rolling window cross validation. Another technique that can be used is forward chaining cross validation.
Step-by-step explanation:
When working with time series data, it is not appropriate to use k-fold cross validation or LOOCV as they assume that the data points are independent and identically distributed (IID). However, time series data violates this assumption because it has a temporal component. Instead, a commonly used cross validation technique for time series data is called rolling window cross validation.
Rolling window cross validation involves splitting the time series data into consecutive overlapping windows. For example, if we have 100 time points and choose a window size of 20, we would have 81 training sets and 81 testing sets. The model is trained on each training set and tested on the corresponding testing set, mimicking the rolling nature of time series data. This allows us to evaluate the model's performance over time.
Another technique that can be used with time series data is forward chaining cross validation. This involves using all data points up until a certain point as the training set, and then using the remaining data point as the testing set. This process is repeated for each data point, ensuring that the model is tested on unseen data that occurs after the training data. This technique is useful when we want to assess how well the model performs in a real-time scenario.