Smart　city　driven　by　Big　Data　and　Internet　of　Things　(IoT)　has　become　a　most　promising　trend　of　the　future.　As　one　important　function　of　smart　city,　event　alert　based　on　time　series　prediction　is　faced　with　the　challenge　of　how　to　extract　and　represent　discriminative　features　of　sensing　knowledge　from　the　massive　sequential　data　generated　by　IoT　devices.　In　this　paper,　a　framework　based　on　sparse　representation　model　(SRM)　for　time　series　prediction　is　proposed　as　an　efficient　approach　to　tackle　this　challenge.　After　dividing　the　over-complete　dictionary　into　upper　and　lower　parts,　the　main　idea　of　SRM　is　to　obtain　the　sparse　representation　of　time　series　based　on　the　upper　part　firstly,　and　then　realize　the　prediction　of　future　values　based　on　the　lower　part.　The　choice　of　different　dictionaries　has　a　significant　impact　on　the　performance　of　SRM.　This　paper　focuses　on　the　study　of　dictionary　construction　strategy　and　summarizes　eight　variants　of　SRM.　Experimental　results　demonstrate　that　SRM　can　deal　with　different　types　of　time　series　prediction　flexibly　and　effectively.