I am used to data sets that dont have a time component. In In reading up on time series data i learned the importance of transforming the data into stationary data before applying the ARIMA model to univariate time series. But what if i have a multivariate data set (meaning: many features potentially explaining one response variable) and i would like to use an advanced ML technique such as a regression random forest? Before applying the random forest, should i first transform the data into a stationary time series?
More details with specific questions below:
-for each point in time ( t=0, 1,....T) i have a value for the response variable y and the features x1, x2....xN) -I need to predict future values of y based on knowing x1, x2,...xN -note:i cannot use time as one of the features in predicting y -by the way: if i disregard any temporal structure in the data, and run the random forest on a randomly chosen training data set, and evaluate the error on the remaining test set i get a pretty good result. Note: since i disregarded the temporal order, my test set observations do not necessarily occur after my training set observations. Questions: 1-Would i maybe get better results if i turned the data into stationary data and then ran the random forest on the transformed data? 2-If so, do i need to apply the same transformation to the y as well as to the x variables? E.g. If to remove a temporal structure in the y i difference the y and remove a seasonal component in the y, do i need to also difference all the x and remove any seasonal component in the x before running random forest? 3-once i have made the series stationary, is it ok to randomly pick a training and test data set without respecting temporal order (ie without having test set observations occur after training set observations)? e features.