I have some problem with the choice of forecast techniques on time series data with a machine learning approach. I want to forecast the CPU usage of the system for exactly one day (length of time serie is one week). I know that there are exist several stochastic forecast approach such as ARIMA, ETS etc. But these techniques can only accept only one target variable for the prediction ("UsageCPU"). Beside only one target variable it would be nice to compute other variables such as UsageMemory, Indicator and Delay in my forecast, because these data have some correlations. Possible forecast techniques could be regression or neural network.
But at this point I have no clue, how to integrate multiple features in my forecast. There are exist some example for neural networks in python and R, where RNN or LTSM have been used and for regression where Decision Tree or Random Forrest have been used. For all these techniques the sliding window approach would be one option, right?
Timestamp UsageCPU UsageMemory Indicator Delay 2014-01-03 21:50:00 3123 1231 1 123 2014-01-03 22:00:00 5123 2355 1 322 2014-01-03 22:10:00 3121 1233 2 321 2014-01-03 22:20:00 2111 1234 2 211 2014-01-03 22:30:00 1000 2222 2 0 2014-01-03 22:40:00 4754 1599 1 0
So, is it required to apply the sliding window approach for all my variables in the dataset? Because I could not found any code example of this question.
Would be nice is someone can help me. Thanks!