FDformer: A Fuzzy Dynamic Transformer-Based Network for Efficient Industrial Time Series Prediction

Abstract

Industrial time series prediction is highly important for the predictive maintenance of Industrial Internet of Things devices. Deep learning methods have demonstrated state-of-the-art (SOTA) performance in the field of time series prediction. However, time series data from complex industrial scenarios often contain substantial uncertainty. This makes it difficult for deterministic deep learning models to achieve accurate predictions. Moreover, existing static methods often fail to meet the real-time requirements of industrial environments. To address the challenges, this study introduces fuzzy learning into deep learning models to overcome the drawbacks of fixed model representations. Therefore, we propose a fuzzy dynamic transformer (FDformer) that can adaptively adjust network depth according to the complexity of individual samples. Subsequently, we design a fuzzy feature extraction mechanism to capture feature information within the fuzzy membership degree, enabling the feature-level fusion of the fuzzy representation with the dynamic depth representation. Finally, we propose a training method for dynamically allocating loss weights, emphasizing the contribution of various samples to different exits, thereby improving the performance of time-series dynamic networks. Experiments on multiple datasets indicate that FDformer achieves minimal computational costs and excellent prediction accuracy across multiple datasets, outperforming SOTA algorithms.