IPatch: A Multi-Resolution Transformer Architecture for Robust Time-Series Forecasting

• URL: http://arxiv.org/abs/2603.24207v1
• Date: Wed, 25 Mar 2026 11:31:32 GMT
• Title: IPatch: A Multi-Resolution Transformer Architecture for Robust Time-Series Forecasting
• Abstract summary: IPatch is a multi-resolution Transformer architecture that integrates point-wise and patch-wise tokens, modeling temporal information at multiple resolutions.<n>Experiments on 7 benchmark datasets demonstrate that IPatch consistently improves forecasting accuracy, to noise, and generalization across various prediction horizons.
• Score: 1.00150464907602
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate forecasting of multivariate time series remains challenging due to the need to capture both short-term fluctuations and long-range temporal dependencies. Transformer-based models have emerged as a powerful approach, but their performance depends critically on the representation of temporal data. Traditional point-wise representations preserve individual time-step information, enabling fine-grained modeling, yet they tend to be computationally expensive and less effective at modeling broader contextual dependencies, limiting their scalability to long sequences. Patch-wise representations aggregate consecutive steps into compact tokens to improve efficiency and model local temporal dynamics, but they often discard fine-grained temporal details that are critical for accurate predictions in volatile or complex time series. We propose IPatch, a multi-resolution Transformer architecture that integrates both point-wise and patch-wise tokens, modeling temporal information at multiple resolutions. Experiments on 7 benchmark datasets demonstrate that IPatch consistently improves forecasting accuracy, robustness to noise, and generalization across various prediction horizons compared to single-representation baselines.

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