# Regression - out-of-sample forecasting

I try to figure out how to deal with my forecasting problem and I am not sure if my understanding is right in this field, so it would be really nice if someone can help me. First of all, my goal is to forecast a time series with regression. Instead of using ARIMA model or other heuristic models I want to focus on machine learning techniques like regressions such as random forest regression, k-nearest-neighbour regression etc.. Here is an overview of the dataset:

        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


The timestamp is increased in steps of 10 minutes and I want to predict the independent variable UsageCPU with the dependent variables UsageMemory, Indicator etc.. At this point i will explain my general knowledge of the prediction part. So for the prediction it is necessary to separate the dataset into training, validation and test sets. For this my dataset that contains 2 whole weeks is separated in 60% training, 20% validation and 20% test. This means for training set I have the first 8 days included and for the validation and the test set I have each 3 days. After that I can train a model in SparkR (the settings are not important).

model <- spark.randomForest(train, UsageMemory ~ UsageMemory, Indicator, Delay,
type = "regression", maxDepth = 30, maxBins = 50, numTrees=50,
impurity="variance", featureSubsetStrategy="all")


So after this I can validate the results with the validation set and compute the RMSE to see the accuracy of the model and which point have to tuned in my model building part. If that is finished I can predict on the test dataset:

predictions <- predict(model, test)


So the prediction works fine, but this is only an in-sample forecast and can not be used to predict for example the next day. In my understanding the in-sample can only used to predict the data in the data set and not to predict future values that can happen tomorrow. So really want to predict for example the next day or only the next 10 minutes / 1 hour, which is only possible to success with the out-of-sample forecasting. I also tried something like this (rolling regression) on the predicted values from random forest, but in my case the rolling regression is only used for evaluating the performance of different regressors with respect to different parameters combinations. So this is in my understanding no out-sample forecasting.

t <- bind(prediction, RollingRegression3 = rollApply(prediction, fun=function(x) mean(UsageCPU), window=6, align='right'))


So in my understanding I need something (maybe lag values?), before the model building process starts. I also read a lot of different papers and books, but there is no clear way how to do it and what are the key points. There is only standing something like t+1, t+n, but right now I do not even know how to do it. Would be really nice if someone can help me, because I tried to figure this out since three month now.