Caret timeslice step size5/21/2023 ![]() Most of these operations are repeated (the last 800 window on day n is the second last 800 window on day n+1). Next we will move to day n+1 and we will compute, yet again, this model over a sequence of 200 sliding windows each of which is of size 800. On day n for a given value of the parameter (mtry), we will train this model over a sequence of 200 sliding windows each of which is of size 800. the best model for each day is selected tuning the parameters over the previous 200 days. To illustrate the inefficiency, consider an even more extreme case – we are selecting the best model every day, using the the above parameters, i.e. ![]() Abundant time series data is generated everywhere, hence, I feel this evolving model approach deserves at least as much attention as the “fit once, live happily thereafter” approach.īack to our discussion. This forward-walking approach has been found useful in trading, but surprisingly, hasn’t been discussed pretty much elsewhere. This is equivalent to saying something like: “Let’s choose the best model each Friday, use the selected model to predict each day for the next week. Then what? What I usually do is to walk the time series forward and repeat these steps at certain intervals. Once the best model is selected, we can forecast the next data point. So far we have dealt with a single model selection. Granted, this approach (of doing things on daily basis) may sound extreme, but it’s useful to illustrate the overhead which is imposed when the model evolves over time, so bear with me. No future-snooping here, because all history points are prior the points being forecasted. Now, for each value of mtry we end up with 200 forecasted points, using the accuracy (or any other metric) we select the best performing model over these 200 points. The test set for a single model is the single point forecast. The training set for each model is the previous 800 points. In other words, for a single value of mtry, we will compute: When the above ntrol is used in training (via the train call), we will end up using 200 models for each set of parameters (each value of mtry in the random forest case). To address this issue, caret provides the timeslice cross validation method: The results are good on the training set, but the performance on the test set, the hold out, is bad. When dealing with time series, using regular cross validation has a future-snooping problem and from my experience general cross validation doesn’t work well in practice for time series data. On each subset, it will train all models (as many models as different values for mtry there are) and finally it will choose the model behaving best over all cross validation folds. According to the algorithm outline, caret will create a few subsets. Let’s assume we are using some form of cross validation. ![]() # 1 mtry numeric #Randomly Selected Predictors For this model, a single parameter, mtry is optimized: So let’s say we are training a random forest.
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