It is an effective means to ensure industrial safety production and improve production efficiency to predict the future trend of equipment operation status and production process based on industrial control system data, and identify potential faults in advance. However, trend prediction algorithms face challenges in understanding the complex correlations within and between sequences in industrial control data. To address this, a time series prediction model for industrial control systems is proposed. The model introduces wavelet transform during data preprocessing to locally capture non-stationary behaviors, abrupt changes, and transient features in both time and frequency domains. It integrates adaptive graph convolution to analyze temporal correlations across sequences, identifying periodic and trend patterns in the data. Finally, a dynamically weighted state-space model, Mamba, is used to uncover multivariate dependencies within each time series. Experiments on seven public datasets and a custom-built industrial control dataset demonstrate that the model reduces MSE by 34% and MAE by 37% compared to seven other prediction models, demonstrating its significant performance advantages.