# Channel, Trend and Periodic-Wise Representation Learning for Multivariate Long-term Time Series Forecasting

arXiv: https://arxiv.org/abs/2509.23583 · HTML: https://mlsanity.com/paper/2509.23583/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2025.

## Built on by (most cited first, 2 of 2)

- [ChannelKAN: Multi-Scale Dual-Domain Channel Prediction via Hybrid CNN-KAN Architecture](https://mlsanity.com/paper/2605.12553/) (2026)
- [Adaptive Oscillatory-State Alignment for Time Series Forecasting](https://mlsanity.com/paper/2606.06010/) (2026)

## Abstract (arXiv)

Downsampling-based methods for time series forecasting have attracted increasing attention due to their superiority in capturing sequence trends. However, this approaches mainly capture dependencies within subsequences but neglect inter-subsequence and inter-channel interactions, which limits forecasting accuracy. To address these limitations, we propose CTPNet, a novel framework that explicitly learns representations from three perspectives: i) inter-channel dependencies, captured by a temporal query-based multi-head attention mechanism; ii) intra-subsequence dependencies, modeled via a Transformer to characterize trend variations; and iii) inter-subsequence dependencies, extracted by reusing the encoder with residual connections to capture global periodic patterns. By jointly integrating these levels, proposed method provides a more holistic representation of temporal dynamics. Extensive experiments demonstrate the superiority of the proposed method.