Time　series　is　mostly　with　a　chaotic　nature　and　non-stationary　characteristic　in　real-word,　which　makes　it　difficult　to　be　modeled　and　predicted　accurately.　To　solve　this　problem,　we　introduce　a　novel　self-organizing　modular　neural　network　based　on　the　empirical　mode　decomposition　with　the　sliding　window　mechanism　(SWEMD-MNN)　for　time　series　prediction.　In　SWEMD-MNN,　the　improved　empirical　mode　decomposition　with　sliding　window　(SWEMD)　is　developed　to　decompose　time　series　online,　which　can　effectively　alleviate　the　limitation　that　the　traditional　EMD-based　models　cannot　handle　the　long　term　or　online　problem　and　end　effect.　Thus,　SWEMD-MNN　can　decompose　time　series　based　on　time　characteristic　effectively　and　dynamically,　and　improve　the　prediction　accuracy　of　the　classical　modular　neural　networks　dividing　time　series　based　on　sample　space.　Then　time　subseries　are　dynamically　assigned　to　the　subnetworks　with　a　single　layer　feedforward　neural　network　using　the　sample　entropy　and　Euclidean　distance　for　learning.　Experimental　investigations　using　benchmark　chaotic　and　real-world　time　series　show　that　SWEMD-MNN　can　decompose　time　series　effectively　and　dynamically,　and　provides　a　better　prediction　accuracy　than　the　fully　coupled　networks　and　other　MNN　models　for　time　series　prediction.&　COPY;　2023　Elsevier　B.V.　All　rights　reserved.