In　many　fields,　time　series　prediction　is　gaining　more　and　more　attention,　e.g.,　air　pollution,　geological　hazards,　and　network　traffic　prediction.　Water　quality　prediction　is　based　on　historical　data　to　predict　future　water　quality.　However,　it　is　difficult　to　learn　a　representation　map　from　a　time　series　that　captures　the　trends　and　fluctuations　to　effectively　remove　noise　from　time　series　data　and　capture　complex　nonlinear　relationships.　To　solve　these　problems,　this　work　proposes　a　time　series　prediction　model,　called　PSGT　for　short,　which　integrates　Patch　Savitsky-Golay　filtering　and　Transformer.　First,　this　work　adopts　a　Patching　method　to　embed　sub-time　series　data　and　obtains　the　trends　and　semantic　information　of　the　time　series.　Second,　it　uses　the　Savitsky-Golay　filtering　to　effectively　remove　the　noise　data　in　the　patch　and　improve　the　prediction　accuracy.　Third,　it　uses　a　Transformer　mechanism　to　address　the　nonlinear　problem　of　water　quality　time　series　and　improve　long-term　prediction　capability.　Two　real-world　datasets　are　utilized　to　evaluate　the　proposed　PSGT,　and　experiments　prove　that　PSGT　performs　better　than　other　benchmark　models　by　at　least　6%.　©　2024　IEEE.