arXiv Machine Learning August 5, 2026 By Jung Min Choi, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme

NPMixer: Hierarchical Neighboring Patch Mixing for Time Series Forecasting

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arXiv:2605. 07476v2 Announce Type: replace Abstract: Multivariate time series forecasting remains a challenge due to the complexity of local temporal dynamics and global dependencies across multiple variables.

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Recovering Lost Details: Multi-Scale Frequency Compensation for Long-Term Time Series Forecasting

arXiv:2609.24229v1 Announce Type: new Abstract: Long-term time series forecasting has made significant progress by leveraging multi-scale information to capture hierarchical temporal patterns and mod...

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arXiv:2608. 04051v1 Announce Type: new Abstract: Real-world time series are often governed by recurring patterns, but their dominant periods may vary across datasets, forecasting settings, and individual input windows.

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arXiv:2606. 03121v1 Announce Type: new Abstract: Multivariate time series forecasting plays a critical role in real-world applications, including weather prediction, stock analysis, and health monitoring.

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SEMixer: Semantics Enhanced MLP-Mixer for Multiscale Mixing and Long-term Time Series Forecasting

arXiv:2602. 16220v2 Announce Type: replace Abstract: Modeling multiscale patterns is crucial for long-term time series forecasting (TSF).

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Recovering Lost Details: Multi-Scale Frequency Compensation for Long-Term Time Series Forecasting

CAMP: A Cycle-Aware Multi-Scale Patch Mixer for Time Series Forecasting

TiWeaver: Unified Temporal Dynamics Modeling via Contextual Patching

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