QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning

Hari Hara Suthan Chittoor, Paul Robert Griffin, Ariel Neufeld, Jayne Thompson, Jayne Thompson, Mile Gu

2025

Abstract

Long-term time series forecasting (LTSF) involves predicting a large number of future values of a time series based on the past values. This is an essential task in a wide range of domains including weather forecasting, stock market analysis and disease outbreak prediction. Over the decades LTSF algorithms have transitioned from statistical models to deep learning models like transformer models. Despite the complex architecture of transformer based LTSF models ‘Are Transformers Effective for Time Series Forecasting? (Zeng et al., 2023)’ showed that simple linear models can outperform the state-of-the-art transformer based LTSF models. Recently, quantum machine learning (QML) is evolving as a domain to enhance the capabilities of classical machine learning models. In this paper we initiate the application of QML to LTSF problems by proposing QuLTSF, a simple hybrid QML model for multivariate LTSF. Through extensive experiments on a widely used weather dataset we show the advantages of QuLTSF over the state-of-the-art classical linear models, in terms of reduced mean squared error and mean absolute error.

Paper Citation

in Harvard Style

Chittoor H., Griffin P., Neufeld A., Thompson J. and Gu M. (2025). QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO; ISBN 978-989-758-737-5, SciTePress, pages 824-829. DOI: 10.5220/0013395500003890

in Bibtex Style

@conference{qaio25,
author={Hari Chittoor and Paul Griffin and Ariel Neufeld and Jayne Thompson and Mile Gu},
title={QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO},
year={2025},
pages={824-829},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013395500003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: QAIO
TI - QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning
SN - 978-989-758-737-5
AU - Chittoor H.
AU - Griffin P.
AU - Neufeld A.
AU - Thompson J.
AU - Gu M.
PY - 2025
SP - 824
EP - 829
DO - 10.5220/0013395500003890
PB - SciTePress