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arXiv Machine Learning By Jonathan H. A. de Carvalho, Filipe C. de L. Duarte, Fernando M. de Paula Neto, Paulo S. G. de Mattos Neto

Quantum-classical hybrid models based on error correction for time series forecasting

Read the original on arXiv Machine Learning →

arXiv:2606. 15213v1 Announce Type: cross Abstract: 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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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