With　the　wide　use　of　service-oriented　architecture　(SOA)　in　academia　and　industry,　the　method　of　recommending　services　based　on　the　Quality-of-Service　(QoS)　has　become　increasingly　important.　To　obtain　the　QoS　attributes　of　Web　services,　many　QoS　prediction　methods　have　been　proposed.　However,　existing　methods　rarely　consider　the　QoS　variance　caused　by　the　location　information　and　the　mutual　influence　between　QoS　attributes　at　the　same　time.　To　tackle　this　problem,　this　study　proposes　a　novel　approach　called　Mul-TSFL　(Multivariate　Time　Series　Forecast　based　on　Location),　which　combines　both　collaborative　filtering　method　and　time　series　model　to　achieve　more　accurate　QoS　prediction　results.　The　experiments　based　on　the　real-world　QoS　data　set　WS-Dream　have　been　conducted　to　verify　the　effectiveness　of　the　proposed　method.　Experimental　results　show　that　our　method　outperforms　the　related　arts.