Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting

Version 1 : Received: 30 September 2021 / Approved: 4 October 2021 / Online: 4 October 2021 (11:11:33 CEST)
Version 2 : Received: 9 October 2021 / Approved: 12 October 2021 / Online: 12 October 2021 (11:41:30 CEST)

A peer-reviewed article of this Preprint also exists.

Fang, Z.; Dowe, D.L.; Peiris, S.; Rosadi, D. Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting. Entropy 2021, 23, 1601. Fang, Z.; Dowe, D.L.; Peiris, S.; Rosadi, D. Minimum Message Length in Hybrid ARMA and LSTM Model Forecasting. Entropy 2021, 23, 1601.

Journal reference: Entropy 2021, 23, 1601
DOI: 10.3390/e23121601

Abstract

We investigate the power of time series analysis based on a variety of information-theoretic approaches from statistics (AIC, BIC) and machine learning (Minimum Message Length) - and we then compare their efficacy with traditional time series model and with hybrids involving deep learning. More specifically, we develop AIC, BIC and Minimum Message Length (MML) ARMA (autoregressive moving average) time series models - with this Bayesian information-theoretic MML ARMA modelling already being new work. We then study deep learning based algorithms in time series forecasting, using Long Short Term Memory (LSTM), and we then combine this with the ARMA modelling to produce a hybrid ARMA-LSTM prediction. Part of the purpose of the use of LSTM is to seek capture any hidden information in the residuals left from the traditional ARMA model. We show that MML not only outperforms earlier statistical approaches to ARMA modelling, but we further show that the hybrid MML ARMA-LSTM models outperform both ARMA models and LSTM models.

Keywords

long short-term memory; minimum message length; time series; neural network; deep learning; Bayesian statistics; probabilistic modeling

Subject

MATHEMATICS & COMPUTER SCIENCE, Probability and Statistics

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