Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

2-2025

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.

Keywords

Quantum Computing, Machine Learning, Time Series Forecasting, Hybrid Model

Discipline

Information Security

Areas of Excellence

Digital transformation

Publication

Proceedings of the 17th International Conference on Agents and Artificial Intelligence, ICAART 2025, Porto, Portugal, February 23-25

Volume

1

First Page

824

Last Page

829

Identifier

10.5220/0000196100003890

Publisher

Scitepress

City or Country

Portugal

Additional URL

https://doi.org/10.5220/0000196100003890

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