I have a model to predict +1 day ahead of this time series.
Looking at the chart you can notice some seasonality every 5 days. I suspect using a moving window as training set could help me making a better prediction.
However I want to programmatically find the best Moving Window Size for my model. Are these approaches below valid? Should I do something different?
Approach 1. I run the model on the historical data, with any possible Window Size, I pick the window size that minimises the prediction error. This approach is simple and fast, but I am afraid it overfits the Window Size to historical that. Right?
Approach 2. I use cross-validation (LOOCV) to get a more realistic prediction error. Is this better/worse than Approach 1?