Multivariate　time　series　classification　is　one　of　the　increasingly　important　issues　in　machine　learning.　Existing　methods　focus　on　establishing　the　global　long-range　dependen-cies　or　discovering　the　local　critical　sequence　fragments.　However,　they　often　ignore　the　combined　information　from　both　global　and　local　features.　In　this　paper,　we　propose　a　novel　network　(called　DA-Net)　based　on　dual　attention　to　mine　the　local???global　features　for　multivariate　time　series　classification.　Specifically,　DA-Net　consists　of　two　distinctive　layers,　i.e.,　the　Squeeze-Excitation　Window　Attention　(SEWA)　layer　and　the　Sparse　Self　-Attention　within　Windows　(SSAW)　layer.　For　the　SEWA　layer,　we　capture　the　local　window-wise　information　by　explicitly　establishing　window　dependencies　to　prioritize　critical　windows.　For　the　SSAW　layer,　we　preserve　rich　activate　scores　with　less　computa-tion　to　widen　the　window　scope　for　capturing　global　long-range　dependencies.　Based　on　the　two　elaborated　layers,　DA-Net　can　mine　critical　local　sequence　fragments　in　the　process　of　establishing　global　long-range　dependencies.　The　experimental　results　show　that　DA　-Net　is　able　to　achieve　competing　performance　with　state-of-the-art　approaches　on　the　mul-tivariate　time　series　classification.　??　2022　Elsevier　Inc.　All　rights　reserved.