You work as a machine learning specialist for a financial services company. You are building a machine learning model to perform futures price prediction. You have trained your model, and you now want to evaluate it to make sure it is not overtrained and can generalize.
Which of the following techniques is the appropriate method to cross-validate your machine learning model?

Answer options:

A.Leave One Out Cross Validation (LOOCV)
B.K-Fold Cross Validation
C.Stratified Cross Validation
D.Time Series Cross Validation

Answer: D
Option A is incorrect. Since we are trying to validate a time series set of data, we need to use a method that uses a rolling origin with day n as training data and day n+1 as test data. The LOOCV approach doesn’t give us this option. (See the article K-Fold and Other Cross-Validation Techniques)
Option B is incorrect. The K-Fold cross validation technique randomizes the test dataset. We cannot randomize our test dataset since we try to validate a time series set of data. Randomized time series data loses its time-related value.
Option C is incorrect. We are trying to cross-validate time series data. We cannot randomize the test data because it will lose its time-related value.
Option D is correct. The Time Series Cross Validation technique is the correct choice for cross-validating a time series dataset. Time series cross validation uses forward chaining, where the origin of the forecast moves forward in time.Day n is training data and day n+1 is test data.
Reference:
Please see the Amazon Machine Learning developer guide titled Cross Validation, and the article K-Fold and Other Cross-Validation Techniques.