Forecasting Inflection Points: Hybrid Methods with Multiscale Machine Learning Algorithms

Julien Chevallier, Bangzhu Zhu () and Lyuyuan Zhang
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Bangzhu Zhu: Nanjing University of Information Science and Technology
Lyuyuan Zhang: University of Melbourne

Computational Economics, 2021, vol. 57, issue 2, No 7, 537-575

Abstract: Abstract This paper investigates hybrid time series forecasting models, which are based on combinations of ensemble empirical mode decomposition and least squares support vector machines. Several algorithms are considered: the genetic algorithm, the grid search, and particle swarm optimization. Theoretical guarantees of prediction accuracy are tested with sine curves. From a numerical testing perspective, we are interested in showing the superiority of one approach to another based on theoretical prediction and time series applications in finance (S&P 500), commodities (WTI oil price), or cryptocurrencies (Bitcoin). The superiority of hybrid models to soft- and hard-computed models is further assessed through a ‘horse race’ and trading performance, as well as through fine-tuning of the algorithms.

Keywords: Genetic algorithms; Ensemble empirical mode decomposition; Least squares support vector machine; Grid Search; Particle swarm optimization (search for similar items in EconPapers)
JEL-codes: C44 G17 Q47 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10614-019-09966-z

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