Water　quality　prediction　refers　to　the　prediction　of　future　water　quality　changes　based　on　past　data.　Traditional　prediction　models　cannot　capture　intricate　and　nonlinear　features.　Typical　machine　learning　methods　extract　nonlinear　characteristics,　but　they　suffer　from　overfitting　problems　due　to　data　noise.　Most　current　deep　learning　models　have　problems　of　gradient　disappearance　and　explosion,　and　often　fail　to　capture　long-term　dependence.　To　solve　above-mentioned　problems,　this　work　proposes　a　multi-indicator　time　series　prediction　method　named　SG-Informer　for　river　water　quality　prediction.　SG-Informer　integrates　the　Savitsky-Golay　filter,　the　ProbSparse　self-attention　mechanism　of　an　encoder,　and　a　generative　style　decoder,　serving　as　data　smoothing　and　noise　elimination,　network　scale　reduction,　and　prediction　speed　improvement,　respectively.　SG-Informer　establishes　a　high-quality　water　quality　time　prediction　model,　which　effectively　predicts　the　future　water　quality　time　series　trend.　Based　on　real-life　data　sets　of　water　quality,　multi-indicator　and　single-indicator　prediction　experiments　are　performed.　Experimental　results　demonstrate　that　the　proposed　SG-Informer　outperforms　several　state-of-the-art　prediction　methods　in　terms　of　prediction　accuracy.　©　2022　IEEE.