用量子机器学习提升长期时间序列预测精度。
QuLTSF: Long-Term Time Series Forecasting with Quantum Machine Learning
- 提出轻量级混合量子模型QuLTSF，融合经典与量子计算优势。
- 在气象数据集上，均方误差和平均绝对误差均优于顶尖线性模型。
- 适合对长序列预测精度要求高的科研与工业场景。
长期时间序列预测（LTSF）是基于历史数据预测大量未来值的重要任务，广泛应用于天气预报、股市分析和疫情预测等领域。尽管深度学习模型如Transformer已广泛应用，但最新研究显示简单线性模型仍可能超越最先进的Transformer模型。本文首次将量子机器学习（QML）引入LTSF问题，提出轻量级混合量子模型QuLTSF，用于多变量长期预测。在广泛使用的气象数据集上进行大量实验表明，QuLTSF在均方误差（MSE）和平均绝对误差（MAE）方面均优于当前最先进的经典线性模型，验证了量子计算在时间序列预测中的潜力。
原文摘要 · Abstract (English)
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.
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