The　pipelined　recurrent　wavelet　neural　network　(PRWNN)　with　nested　structure　has　been　widely　used　for　time　series　prediction.　However,　selecting　the　appropriate　network　structure　for　different　problems　is　still　an　unsolved　problem.　To　solve　this　issue,　the　self-organizing　PRWNN　(SPRWNN)　is　proposed.　Different　from　the　PRWNN　with　fixed　structure,　the　number　of　modules　and　hidden　neurons　of　SPRWNN　can　be　automatically　determined　during　the　training　process.　Firstly,　the　number　of　hidden　neurons　in　each　module　of　the　SPRWNN　is　adjusted　by　the　spiking　strength　(SS)　of　nodes.　Then,　the　module　growth　mechanism　is　designed　by　measuring　the　performance　of　the　SPRWNN.　Furthermore,　the　convergence　of　SPRWNN　is　proved　theoretically.　The　experimental　results　show　that　the　SPRWNN　can　not　only　automatically　adjust　the　network　structure,　but　also　improves　the　prediction　accuracy　by　about　40%　as　compared　with　the　PRWNN　with　fixed　structure.