Model parameter selection for time series data
<p>For model parameter selection, we always make a grid-search with cross validation to test which parameters are better than others.</p>

<p>It's right for general training data, like <a href="https://www.kaggle.com/c/shelter-animal-outcomes" rel="nofollow">this one</a>, but if data has time relationship with each other, like sells over days or stock over days, is that wrong to do cross validation directly?</p>

<p>As cross validation will use kFold which split randomly in training data, which means for time series data, recent days info will be used for training on old days.</p>

<p>My Question is, how to do parameter selection, or cross validation on time series data?</p>