In wastewater treatment processes, the efficient modeling of key water quality parameters is crucial for achieving process control optimization, anomaly detection, and decision support. However, the process data generally exhibits characteristics such as temporal dependence, multivariable coupling, and non-stationarity under varying operating conditions, posing significant challenges to accurate modeling. To address these issues, this paper proposes a Lightweight Multivariate Time Series Prediction Method for Wastewater Treatment based on the Stationary Wavelet Transform (SWT) and Collaborative Attention (CA) mechanism. This model first performed multi-scale decomposition on wastewater data and used the stationary wavelet transform to extract data features from sequences at different scales. Subsequently, a collaborative attention mechanism based on geometric attention and sparse attention was constructed to effectively capture the complex coupling relationships and temporal features among key water quality parameters. Finally, the features reconstructed via inverse wavelet transform were mapped to the final prediction results through a dual-prediction layer. The model was trained and validated on a measured dataset from a wastewater treatment plant in Dongguan, with multi-step prediction tasks and partial data visualization analyses conducted. Experimental results show that, in the 24-step multi-output prediction tasks, the proposed model achieves a reduction of 9.15% to 37.70% in multi-output root mean square deviation (RMSSD) compared to benchmark models. In other prediction tasks, its accuracy ranks second only to TimesNet, which has a significantly larger parameter scale. These results demonstrate an effective balance between lightweight design and high accuracy, thereby validating the efficacy of the proposed model for time-series prediction in wastewater treatment.