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Description
We introduce Non-Linear Hybrid Shrinkage (NLHS) as a holistic model for forecast combination, shrinkage and selection. NLHS first determines the selection of forecasters based on information criteria such as forward feature selection and stores the selection status of forecasters in a selection vector. Depending on the selection status, the estimated optimal weights of the forecasters are either shrunk to zero or equal weights by the least absolute shrinkage and selection operator (LASSO). Among benchmark algorithms such as simple average, optimal weights, or linear and LASSO-based shrinkage methods, NLHS is superior for a larger number of forecasters, as shown in simulation-based experiments.
Recommended Citation
Schulz, Felix, "Non-Linear Hybrid Shrinkage of Weights for Forecast Selection and Combination" (2022). Wirtschaftsinformatik 2022 Proceedings. 7.
https://aisel.aisnet.org/wi2022/business_analytics/business_analytics/7
Non-Linear Hybrid Shrinkage of Weights for Forecast Selection and Combination
We introduce Non-Linear Hybrid Shrinkage (NLHS) as a holistic model for forecast combination, shrinkage and selection. NLHS first determines the selection of forecasters based on information criteria such as forward feature selection and stores the selection status of forecasters in a selection vector. Depending on the selection status, the estimated optimal weights of the forecasters are either shrunk to zero or equal weights by the least absolute shrinkage and selection operator (LASSO). Among benchmark algorithms such as simple average, optimal weights, or linear and LASSO-based shrinkage methods, NLHS is superior for a larger number of forecasters, as shown in simulation-based experiments.