Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion release_a7kkx7jbo5cyplozwiyaywvzda

by Pieter Cawood, Terence van Zyl

Released as a article .

2022  

Abstract

Techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential-Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base models for different ensembles. We compare against some state of the art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting data set of 100,000 time-series, and the results show that the Feature-based Forecast Model Averaging (FFORMA), on average, is the best technique for late data fusion with the ES-RNN. However, considering the M4's Daily subset of data, stacking was the only successful ensemble at dealing with the case where all base model performances are similar. Our experimental results indicate that we attain state of the art forecasting results compared to N-BEATS as a benchmark. We conclude that model averaging is a more robust ensemble than model selection and stacking strategies. Further, the results show that gradient boosting is superior for implementing ensemble learning strategies.
In text/plain format

Archived Files and Locations

application/pdf  13.5 MB
file_56peckguwnelxk72sxrysxbupa
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2022-07-19
Version   v3
Language   en ?
arXiv  2203.03279v3
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 808b74d0-276f-45fc-9277-8850d88ea3ad
API URL: JSON