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

TL;DR

This paper introduces QuLTSF, a hybrid quantum machine learning model for long-term multivariate time series forecasting, demonstrating its advantages over classical linear models in weather data prediction.

Contribution

The paper pioneers the application of quantum machine learning to long-term time series forecasting with a novel hybrid model, QuLTSF.

Findings

QuLTSF outperforms classical linear models in mean squared error.

QuLTSF reduces mean absolute error compared to state-of-the-art models.

Quantum-enhanced forecasting shows promise for complex time series prediction.

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.