While I have achieved success using Statistical Methods, I wish to compare the same with ML/DL Techniques. Following suggestions on this thread, I looked into the M5 Competition and the Winner's Submission, but soon realized something: Do Random Forest Models capture Large seasonality efficiently?
The submissions created variables on lagged values, rolling averages, date parts etc. but in order to capture Annual Seasonality they would require lags up to the order of 365; following SARIMA Order, maybe even more. So, how would you apply a Random Forest method in order to "capture seasonality" of such high order. While this is plausible by de-seasoning the series before ingesting in model, I was more curious to figure out how would one do so using only Random Forest! The same question expands to Neural Nets as well! Please share your opinions on the same!!