English(EN)Multivariate Time Series Forecasting with Adaptive Non-Local Observables
新框架和量子模型推动时间序列预测发展 · 跟踪 5 个来源
作者PulseAugur 编辑部·[5 个来源]·
研究人员正通过新框架和模型推动时间序列预测的发展。其中一种方法 WrapFlow 使用连续时间建模和标记化来处理不规则数据，取得了最先进的结果。另一项进展涉及量子神经网络，MTSF-ANO 集成了变分量子电路和自适应非局部可观测量，以提高预测准确性。此外，受大型语言模型启发的基金模型概念正在被探索用于时间序列预测，提供了一种可以通过微调进一步增强的统一方法。
AI
arXiv:2607.28035v1 Announce Type: new Abstract: Irregular multivariate time series are widely encountered in applications such as healthcare monitoring, human activity recognition, and environmental sensing. Their core challenges stem from asynchronous observations, non-uniform s…
arXiv cs.AI
TIER_1English(EN)·Yu-Ting Lee, Huan-Hsin Tseng, Samuel Yen-Chi Chen·
arXiv:2607.24399v1 Announce Type: cross Abstract: 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 measureme…
arXiv cs.LG
TIER_1English(EN)·Morad Laglil, Bertrand Pracca, Emilie Devijver, Eric Gaussier·
arXiv:2607.23146v1 Announce Type: new Abstract: Inspired by recent breakthroughs in large language models for natural language processing, foundation models have emerged as a promising paradigm for zero-shot time series forecasting, enabling accurate predictions on datasets never…
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. We propose…
arXiv:2607.23165v1 Announce Type: new Abstract: We propose ABF-T-GLCP, a model-agnostic framework for forecasting and uncertainty quantification in nonstationary multivariate time series. The central idea is to learn an adaptive predictive state representation for point forecasti…
