Exformer: A Transformer-Based Forecasting Framework with Temporal Knowledge Assistance for Multistep Multivariate Influent Prediction in Wastewater Treatment Plants

However, due to the seasonality, complex multivariate nexus and inherent disturbances, the predictive performance of WWTP influent is often unsatisfactory, particularly for multistep forecasting. This limitation usually frustrates the practical utility of such models for operational guidance and decision-making.

To address these challenges, this study proposes a Transformer-based forecasting framework called Exformer. The framework employs a Channel-Independent (CI) representation strategy to preserve variable-specific temporal characteristics and reduce cross-variable interference. In addition, a Temporal Knowledge Guided Attention (TKGA) mechanism incorporates temporal prior knowledge into the attention process to enhance long-range dependency modeling.