Modular　neural　networks　have　shown　distinct　advantages　in　many　regimes　due　to　the　＂modular　design＂.　However,　how　to　design　an　effective　decomposition　algorithm　for　improving　the　prediction　accuracy　is　still　a　challenge　for　time　series　prediction.　In　this　article,　a　novel　modular　neural　network　model　based　on　empirical　mode　decomposition　(EMD)　technique　and　multi-view　learning　(MNN-EMDMVL)　is　introduced　for　time　series　prediction,　which　can　simultaneously　and　effectively　decompose　time　series　and　improve　the　prediction　accuracy　of　time　series.　Most　of　modular　neural　networks　divide　time　series　based　on　sample　space　using　clustering　algorithm　without　considering　its　time　characteristic,　which　has　an　undesirable　effect　on　prediction　accuracy.　Therefore,　the　structure　of　the　proposed　model　consists　of　two　parts:　(1)　time　series　decomposition,　which　can　decompose　time　series　into　several　subseries.　(2)　network　learning,　which　can　further　improve　the　prediction　performance　of　model.　Firstly,　this　paper　employs　EMD　technique　to　decompose　time　series　into　several　subseries　based　on　the　local　characteristic　time　scale,　which　can　help　subnetworks　to　learn　the　decomposed　subseries　more　easily.　But　some　subseries　with　high　frequency　feature　fluctuates　greatly,　and　changes　fleetly,　which　may　restrict　the　performance　of　subnetworks.　Therefore,　a　novel　multi-view　learning　with　winner-take-all　strategy　(MVWTAS)　is　proposed　to　further　improve　the　prediction　performance　of　subnetworks.　In　MVWTAS,　one　or　more　views　(features)　of　subseries　with　high　frequency　are　selected　as　the　auxiliary　tasks　and　simultaneously　learned.　Then,　the　＂best　model＂　can　be　obtained　from　the　auxiliary　and　main　tasks　in　each　time　period　of　the　subseries　using　winner-take-all　strategy,　thereby　improving　the　prediction　accuracy　of　the　subseries　with　high　frequency.　Finally,　experimental　investigations　using　two　benchmark　time　series　and　two　real-world　time　series　show　that　compared　with　other　modular　neural　networks　and　single　modes,　MNN-EMDMVL　has　the　distinct　advantage　in　the　terms　of　prediction　performance　both　by　time　series　decomposition　using　EMD　and　the　developed　multi-view　learning　with　winner-take-all　strategy.