CP损失：时间序列预测的通道感知感知损失

Multi-channel time-series data, prevalent across diverse applications, is characterized by significant heterogeneity in its different channels. However, existing forecasting models are typically guided by channel-agnostic loss functions like MSE, which apply a uniform metric across all channels. This often leads to models failing to capture channel-specific dynamics such as sharp fluctuations or trend shifts. To address this, a Channel-wise Perceptual Loss (CP Loss) is proposed. The core idea of CP Loss is to dynamically adjust each channel's contribution to the total loss based on its specific characteristics. Specifically, CP Loss introduces a mechanism to identify and quantify the dynamic complexity of individual channels, for instance, by analyzing their variance, frequency components, or rates of change. Channels exhibiting more pronounced fluctuations or containing critical trend shifts are assigned higher weights by CP Loss, thereby encouraging the model to allocate more learning resources to these channels for more accurate predictions. Conversely, channels with relatively smoother changes receive proportionally lower weights. This channel-level personalized treatment enables the model to better adapt to the intrinsic properties of different channels, overcoming the limitations imposed by a 'one-size-fits-all' loss calculation approach. By doing so, CP Loss aims to improve the overall performance of multi-channel time-series forecasting models, particularly when dealing with datasets featuring high degrees of heterogeneity, allowing for more accurate capture of crucial local dynamics and global trends. Experimental results demonstrate that models employing CP Loss achieve superior prediction accuracy and robustness compared to traditional loss functions across multiple benchmark datasets.