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　uses　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　the　time　series　data　and　investigate　complex　nonlinear　relationships.　To　solve　these　problems,　this　work　proposes　a　time　series　prediction　model,　called　DPSGT　for　short,　which　integrates　Dual　Patch　Savitsky-Golay　filtering　and　Transformer.　First,　DPSGT　adopts　the　SG　filtering　to　decompose　the　time　series　data　and　reduce　the　noise　interference　to　improve　long-term　prediction　capabilities.　Second,　to　tackle　the　limitation　of　temporal　representation　capability,　DPSGT　adopts　dual　patches　to　ravel　temporal　series　into　local　and　global　patches,　which　can　tackle　local　semantic　information　and　enlarge　the　receptive　field.　Third,　it　utilizes　a　transformer　mechanism　to　address　the　nonlinear　problem　of　the　water　quality　time　series　and　improve　the　accuracy　of　the　prediction.　Two　real-world　datasets　are　utilized　to　evaluate　the　proposed　DPSGT,　and　experiments　prove　that　DPSGT　improves　RMSE,　MAE,　MAPE,　and　R2　by　6%,　5%,　8%,　and　7%,　respectively,　compared　with　other　benchmark　models.　　©　2014　IEEE.