Minimum message length in hybrid arma and lstm model forecasting

Fang, Zheng and Dowe, David L. and Peiris, Shelton and Rosadi, Dedi (2021) Minimum message length in hybrid arma and lstm model forecasting. Entropy, 23 (12). ISSN 10994300

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Abstract

Modeling and analysis of time series are important in applications including economics, engineering, environmental science and social science. Selecting the best time series model with accurate parameters in forecasting is a challenging objective for scientists and academic researchers. Hybrid models combining neural networks and traditional Autoregressive Moving Average (ARMA) models are being used to improve the accuracy of modeling and forecasting time series. Most of the existing time series models are selected by information-theoretic approaches, such as AIC, BIC, and HQ. This paper revisits a model selection technique based on Minimum Message Length (MML) and investigates its use in hybrid time series analysis. MML is a Bayesian information-theoretic approach and has been used in selecting the best ARMA model. We utilize the long short-term memory (LSTM) approach to construct a hybrid ARMA-LSTM model and show that MML performs better than AIC, BIC, and HQ in selecting the model—both in the traditional ARMA models (without LSTM) and with hybrid ARMA-LSTM models. These results held on simulated data and both real-world datasets that we considered.We also develop a simple MML ARIMA model. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Item Type: Article
Additional Information: Cited by: 9; All Open Access, Gold Open Access
Uncontrolled Keywords: long short-term memory; minimum message length; time series; neural network; deep learning; Bayesian statistics; probabilistic modeling
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Mathematics and Natural Sciences > Mathematics Department
Depositing User: Sri JUNANDI
Date Deposited: 30 Oct 2024 02:05
Last Modified: 30 Oct 2024 02:05
URI: https://ir.lib.ugm.ac.id/id/eprint/8425

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