Cross Validated
2022-10-08 15:00 UTC
By stolbert
AI-113-20221008-social-media-c646161d
Random Forest Models for Time Series and Cross Validation
If one were to build a model using a random forest model that uses lagged variables, for simplicity we'll describe this just using a single feature describing lag 1: $x_{t-1}$ . Which attempts to predict $x_{t}$ Will this model still be subject to standard time series CV rules? I believe the feature vector from one instance to another will be independant and therefore a forecast of rolling origin isn't required and standard K-Fold can apply? Is there any issues that can arise from not doing a rolling origin CV under this context?
If one were to build a model using a random forest model that uses lagged variables, for simplicity we'll describe this just using a single feature describing lag 1: $x_{t-1}$ . Which attempts to predict $x_{t}$ Will this model still be subject to standard time series CV rules? I believe the feature vector from one instance to another will be independant and therefore a forecast of rolling origin isn't required and standard K-Fold can apply? Is there any issues that can arise from not doing a rolling origin CV under this context?
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Cross Validated
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