Water　quality　monitoring　is　an　essential　part　of　water　big　data　analysis.　Spatiotemporal　variations　of　water　quality　and　constraints　on　measurement　make　it　very　complex.　The　objective　of　this　study　is　to　establish　a　water　quality　data　cleaning　framework　based　on　time　series,　in　order　to　clean　the　water　quality　data　of　the　Gaobeidian　Sewage　Treatment　Plant　inlet　in　Beijing.　Pauta　criterion　was　used　to　deal　with　single　water　quality　indicator.　For　abnormal　values　and　missing　values　that　are　discontinuously　distributed　over　time,　the　average　of　the　non-abnormal　data　for　three　days　before　and　after　was　used　to　fill　it;　For　abnormal　values　and　missing　values　that　are　continuously　distributed　over　time,　using　the　Non-Linear　decreasing　inertia　weight　particle　swarm　algorithm　(NLDIW-PSO)　based　optimal　Support　Vector　Regression　(SVR)　to　forecast.　And　Pearson＇s　correlation　coefficient　was　used　to　reduce　the　dimension　of　the　inputs　of　the　model,　k-fold　cross　validation　was　also　used　to　train　the　model.　The　performance　of　the　model　was　evaluated　in　terms　of　the　coefficient　of　determination　(R2),　Pearson＇s　correlation　coefficient.　Water　quality　data　of　Gaobeidian　wastewater　treatment　inlet　in　Beijing,　China　was　taken　as　the　study　case　to　examine　effectiveness　of　this　approach.　The　experiment　results　also　revealed　that　the　proposed　model　has　advantages　of　stability　and　time　reduction　in　comparison　with　other　data-driven　models　including　traditional　BP　ANN,　Bayesian　network　model　and　Decision　Tree　model.　And　this　framework　can　be　used　as　an　effective　approach　to　deal　with　General　time　series　data.