Transformer　has　gained　widespread　adoption　in　modeling　time　series　due　to　the　exceptional　ability　of　its　self-attention　mechanism　in　capturing　long-range　dependencies.　However,　when　processing　time　series　data　with　numerous　variates,　the　vanilla　self-attention　mechanism　tends　to　distribute　attention　weights　evenly　and　smoothly,　causing　row-homogenization　in　attention　maps　and　further　hampering　time　series　forecasting.　To　tackle　this　issue,　we　propose　an　advanced　Transformer　architecture　entitled　SDformer,　which　designs　two　novel　modules,　Spectral-Filter-Transform　(SFT)　and　Dynamic-Directional-Attention　(DDA),　and　integrates　them　into　the　encoder　of　Transformer　to　achieve　more　intensive　attention　allocation.　Specifically,　the　SFT　module　utilizes　the　Fast　Fourier　Transform　to　select　the　most　prominent　frequencies,　along　with　a　HammingWindow　to　smooth　and　denoise　the　filtered　series　data;　The　DDA　module　applies　a　specialized　kernel　function　to　the　query　and　key　vectors　projected　from　the　denoised　data,　concentrating　this　innovative　attention　mechanism　more　effectively　on　the　most　informative　variates　to　obtain　a　sharper　attention　distribution.　These　two　modules　jointly　enable　attention　weights　to　be　more　salient　among　numerous　variates,　which　in　turn　enhances　the　attention＇s　ability　to　capture　multivariate　correlations,　improving　the　performance　in　forecasting.　Extensive　experiments　on　public　datasets　demonstrate　its　superior　performance　over　other　state-of-the-art　models.　Code　is　available　at　https://github.com/zhouziyu02/SDformer.