Statistical models in time series forecasting have long been challenged to be superseded by the advent of deep learning models.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.
Sadler, Bivin; Dey, Dhruba; Nguyen, Duy; and Weeda, Tavin
"A Hybrid Ensemble of Learning Models,"
SMU Data Science Review: Vol. 7:
2, Article 1.
Available at: https://scholar.smu.edu/datasciencereview/vol7/iss2/1
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