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Tree-based regression within a hidden Markov model framework
Last modified: 2023-06-20
Abstract
While tree-based regression methods are popular in practice, they miss a
time series component. We thus combine regression trees with hidden Markov models (HMMs) and construct a hybrid model that can effectively capture serial correlation and the complex dependencies between the input and output variables, while also providing interpretable results. In a case study, we demonstrate that such an approach offers a powerful and flexible tool for modeling financial data. However, the presented method can be employed in many more fields, e.g. in ecology or sports.
time series component. We thus combine regression trees with hidden Markov models (HMMs) and construct a hybrid model that can effectively capture serial correlation and the complex dependencies between the input and output variables, while also providing interpretable results. In a case study, we demonstrate that such an approach offers a powerful and flexible tool for modeling financial data. However, the presented method can be employed in many more fields, e.g. in ecology or sports.