A Hybrid Ensemble of Learning Models

Authors

Bivin Sadler, Southern Methodist UniversityFollow
Dhruba Dey, Southern Methodist UniversityFollow
Duy Nguyen, Southern Methodist UniversityFollow
Tavin Weeda, Southern Methodist UniversityFollow

Abstract

Statistical models in time series forecasting have long been challenged to be superseded by the advent of deep learning models. This research proposes a new hybrid ensemble of forecasting models that combines the strengths of several strong candidates from these two model types. The proposed ensemble aims to improve the accuracy of forecasts and reduce computational complexity by leveraging the strengths of each candidate model.

Recommended Citation

Sadler, Bivin; Dey, Dhruba; Nguyen, Duy; and Weeda, Tavin (2023) "A Hybrid Ensemble of Learning Models," SMU Data Science Review: Vol. 7: No. 2, Article 1.
Available at: https://scholar.smu.edu/datasciencereview/vol7/iss2/1

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License