Optimal Time Series Forecasting Through the GARMA Model

Adel Hassan A. Gadhi, Shelton Peiris (), David Allen and Richard Hunt
Additional contact information
Adel Hassan A. Gadhi: School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
Shelton Peiris: School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
Richard Hunt: School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia

Econometrics, 2025, vol. 13, issue 1, 1-23

Abstract: This paper examines the use of machine learning methods in modeling and forecasting time series with long memory through GARMA. By employing rigorous model selection criteria through simulation study, we find that the hybrid GARMA-LSTM model outperforms traditional approaches in forecasting long-memory time series. This characteristic is confirmed using popular datasets such as sunspot data and Australian beer production data. This approach provides a robust framework for accurate and reliable forecasting in long-memory time series. Additionally, we compare the GARMA-LSTM model with other implemented models, such as GARMA, TBATS, ARIMA, and ANN, highlighting its ability to address both long-memory and non-linear dynamics. Finally, we discuss the representativeness of the datasets selected and the adaptability of the proposed hybrid model to various time series scenarios.

Keywords: GARMA; sunspot; non-integer seasonality; GARMA-LSTM; time series models; long memory; hybrid; ausbeer; forecasting (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2225-1146/13/1/3/pdf (application/pdf)
https://www.mdpi.com/2225-1146/13/1/3/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:13:y:2025:i:1:p:3-:d:1562553

Access Statistics for this article

Econometrics is currently edited by Ms. Jasmine Liu

More articles in Econometrics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().