A　new　strategy　for　modeling　of　chaotic　systems　is　presented,　which　is　based　on　the　combination　of　the　stationary　wavelet　transform　and　Recurrent　Least　Squares　Support　Vector　Machines　(RLS-SVM).　The　stationary　wavelet　transform　provide　a　sensible　decomposition　of　the　data　so　that　the　underlying　temporal　structures　of　the　original　time　series　become　more　tractable.　The　similarity　of　dynamic　invariants　between　the　origin　and　generated　time　series　shows　that　the　proposed　method　can　capture　the　dynamics　of　the　chaotic　time　series　effectively.