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

2026-08-04

AI Summary

Existing time series forecasting methods struggle with time-varying periodic patterns or coexisting multiple cycles and typically apply a uniform processing strategy to all patches, ignoring their relative importance with respect to the prediction horizon. To address these limitations, this work proposes CAMP, a novel framework that incorporates an adaptive periodicity learning module to identify dominant frequencies for each input window and generate corresponding historical and future periodic components. It further introduces a horizon-guided Patch Mixer for position-aware context fusion and jointly models de-seasonalized residual dynamics across multiple scales. CAMP is the first method to achieve input-window-level adaptive periodic modeling, position-aware patch refinement, and multi-resolution residual learning in a unified architecture. Experiments demonstrate that CAMP achieves the best average MSE and MAE on six out of seven long-term forecasting benchmarks and attains the highest MSE win rate across 16 settings on four PEMS traffic datasets.

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. Existing cycle-aware forecasters commonly rely on a single period selected at the dataset level, which can be restrictive when periodic behavior changes over time or when multiple cycles coexist. Moreover, patch-based models typically process all patch positions uni- formly, although patches farther from the forecast boundary may require broader contextual refinement, while recent patches contain information that should be preserved more directly. Af- ter cyclic behavior is removed, the remaining dynamics may also span multiple temporal resolutions and cannot be adequately de- scribed at a single scale. We introduce CAMP, a Cycle-Aware Multi-Scale Patch Mixer designed to address these challenges. The Adaptive Cycle Learning module identifies dominant fre- quencies separately for each input window and generates both historical and future cyclic components without requiring a pre- defined cycle length. The Horizon-Guided Patch Mixer intro- duces position-dependent refinement, allowing earlier patches to incorporate broader temporal context while preserving infor- mation close to the forecast boundary. CAMP further models the de-cycled residual through temporally aligned multi-resolution representations, enabling complementary dynamics at different scales to be captured within one forecasting framework. Across seven long-term forecasting benchmarks, CAMP achieves the best average MSE on six datasets and the best or tied-best MAE on six. It also obtains the highest MSE win count across sixteen settings on four PEMS traffic benchmarks.

Problem

• time series forecasting
• cycle-aware modeling
• multi-scale representation
• patch-based models
• periodic patterns

Innovation

• Cycle-aware forecasting
• Multi-scale modeling
• Patch-based architecture
• Adaptive period learning
• Time series forecasting