arXiv Machine Learning June 16, 2026 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Aug 4

QARIMA: A Quantum Approach To Classical Time Series Analysis

arXiv:2604. 08277v3 Announce Type: replace-cross Abstract: We present QARIMA, a quantum state-similarity-based reconstruction of the classical ARIMA modelling pipeline.

By Nishikanta Mohanty, Bikash K. Behera, Badshah Mukherjee, Pravat Dash, Giuseppe Sergioli, Roberto Giuntini

safety

arXiv AI

Jul 21

A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations

arXiv:2607. 16358v1 Announce Type: cross Abstract: This paper presents a unified quantum-classical hybrid framework for multi-horizon time-series forecasting, introducing two model variants Quantum Reservoir Forecaster (QRC-F) and Variational Quantum Forecaster (VQF-F).

By Sanjay Chakraborty, Fredrik Heintz

efficiency benchmarks

arXiv AI

Jul 28

Multivariate Time Series Forecasting with Adaptive Non-Local Observables

arXiv:2607. 24399v1 Announce Type: cross Abstract: Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data.

By Yu-Ting Lee, Huan-Hsin Tseng, Samuel Yen-Chi Chen

Hugging Face Trending Papers

Jul 27

Multivariate Time Series Forecasting with Adaptive Non-Local Observables

Multivariate time series forecasting (MTSF) predicts future values of multiple variables from historical data. While quantum neural networks have been increasingly applied to this task, they typically rely on fixed local measurements, which restrict their expressivity.

arXiv AI

Jul 7

A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference

arXiv:2603. 09789v3 Announce Type: replace-cross Abstract: Accurate financial volatility forecasting is crucial but challenged by the non-linear, highly correlated nature of market data.

By Yixiong Chen

efficiency

arXiv Machine Learning

Jul 23

QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting

arXiv:2605. 18333v2 Announce Type: replace-cross Abstract: Accurate and efficient time-series forecasting remains a challenging problem for both classical and quantum neural architectures, particularly in multivariate environmental settings.

By Alberto Marchisio, Aayan Ebrahim, Nouhaila Innan, Muhammad Kashif, Muhammad Shafique

benchmarks

Related stories

QARIMA: A Quantum Approach To Classical Time Series Analysis

A Quantum-Classical Hybrid Framework for Multivariate Time-Series Forecasting Complexity-Fidelity Trade-offs and Limitations

Multivariate Time Series Forecasting with Adaptive Non-Local Observables

Multivariate Time Series Forecasting with Adaptive Non-Local Observables

A Hybrid Quantum Circuit Born Machine Framework for Financial Volatility Forecasting: Quantum-Assisted Training and Classical Inference

QLIF-CAST: Quantum Leaky-Integrate-and-Fire for Time-Series Weather Forecasting