AMR-Net: Adaptive Temporal-Channel Multiresolution Network for Industrial Time-Series Prediction

Accurate and fast industrial time-series prediction is essential for safe and reliable operation of industrial equipment. Recent deep learning methods enable extracting complex temporal patterns by utilizing large-scale parameters and multiresolution feature extraction. However, they cause substantial computational complexity and limit their application at the edge. In this article, we design an adaptive temporal-channel multiresolution network (AMR-Net) that dynamically adjusts time-series resolution to avoid redundant computation. The motivation is that low-resolution feature representations are sufficient for predicting “easy” samples, whereas “hard” samples require high-resolution features to capture fine-grained information. For the AMR-Net, time series are initially input through a temporal-channel resolution decomposition (TCRD) module, which efficiently extracts low-resolution representations. Samples exhibiting high prediction confidence are expedited through early exit mechanisms, avoiding further processing. Meanwhile, high-resolution subnetworks capture the fine-grained information to discern the “hard” samples. Experiments on CMAPSS and N-CMAPSS datasets demonstrate that AMR-Net can improve computational speed by 15x while maintaining high accuracy.