Time series forecasting largely benefits from combining the strengths of different models, especially using a scheme where a model corrects another model by capturing supplementary patterns from forecasting errors. Concurrently, quantum models are providing a means to augment the classical capacity, including in time series forecasting, by acting alongside classical models in hybrid architectures. In this work, we propose the first forecasting system based on error correction that jointly uses quantum and classical models. Here, quantum models first extract patterns by exploring quantum phenomena, and classical models capture the remaining patterns from the quantum errors. Compared to classical single models and classical-classical hybrid models based on error correction, the complementary capacity that emerges from this quantum-classical system provided the best results in most of the addressed problems. Therefore, this work paves the way to introduce quantum models in established hybridization schemes for time series forecasting.

翻译：时间序列预测在很大程度上受益于不同模型的协同优势，尤其是一种通过从预测误差中捕获补充模式来让一个模型纠正另一个模型的方案。与此同时，量子模型正通过与经典模型在混合架构中协同作用，为增强经典能力（包括时间序列预测）提供手段。在本工作中，我们提出了首个基于纠错、联合使用量子与经典模型的预测系统。在此系统中，量子模型首先通过探索量子现象提取模式，随后经典模型从量子误差中捕获剩余模式。与纯经典单一模型及基于纠错的经典-经典混合模型相比，这一量-经系统所涌现的互补能力在大多数已解决问题中均取得了最佳结果。因此，本工作为将量子模型引入时间序列预测的经典混合框架开辟了道路。