What is an appropriate cross-validation technique for time series data?
I have a daily 4 years time series data and fitting a SVM model by MATLAB R2015b:
SVMModel = fitcsvm(Input, binary_output,'KernelFunction','RBF','BoxConstraint',1); CVSVMModel = crossval(SVMModel); z = kfoldLoss(CVSVMModel)
This a binary classification problem. As default I used 10-fold cross validation, but because of the random nature of this method I think this is not suitable for time series data.
- Is it better to use other techniques like sliding window validation as discussed here?
- How we can implement these techniques in MATLAB?
- Are there any predefined functions for other proposed techniques?