Channel‑wise Perceptual Loss Proposed for Multi‑Channel Time Series Forecasting

Global: Channel‑wise Perceptual Loss Proposed for Multi‑Channel Time Series Forecasting

Background and Motivation

On Jan. 25, 2026, a team of researchers including Yaohua Zha, Chunlin Fan, Peiyuan Liu, Yong Jiang, Tao Dai, Hai Wu, and Shu‑Tao Xia submitted a paper to arXiv that introduces a Channel‑wise Perceptual Loss (CP Loss) for time‑series forecasting. The work targets multi‑channel datasets that exhibit significant heterogeneity across individual channels.

Limitations of Existing Approaches

According to the authors, most forecasting models rely on channel‑agnostic loss functions such as mean‑squared error (MSE), which apply a uniform metric to all channels. This uniformity can prevent models from capturing channel‑specific dynamics, including sharp fluctuations or abrupt trend shifts.

Proposed Method

The paper proposes CP Loss, which learns a distinct perceptual space for each channel. In this space, loss is computed after decomposing the raw signal with a learnable channel‑wise filter that produces disentangled multi‑scale representations tailored to each channel’s characteristics.

Technical Implementation

The authors design a filter that operates jointly with the primary forecasting model, ensuring that the perceptual spaces are explicitly oriented toward the prediction task. Loss calculations are performed within these channel‑specific spaces, guiding the model to better respect individual channel behaviors.

Joint Optimization Strategy

Joint optimization of the filter and forecasting model is emphasized as a core contribution, allowing the learned representations to evolve in tandem with forecasting performance.

Potential Applications

By addressing channel heterogeneity, CP Loss could improve forecasting accuracy in domains such as finance, climate modeling, and industrial sensor networks, where multi‑channel time series are common.

Availability and Publication Details

The paper, listed under the Machine Learning (cs.LG) and Artificial Intelligence (cs.AI) categories, is available as a 576 KB PDF on arXiv. The authors have also provided code via a public URL referenced in the abstract.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.