The　machine　downtime　occurring　during　routine　production　(MDT_RP)　because　of　recessive　disturbances　(RecDs)　can　cause　huge　economic　losses　and　slow　down　production.　In　modern　industries,　condition　monitoring,　prognosis,　and　maintenance　policies　are　widely　applied　to　minimize　machine　failures　caused　by　dominant　disturbances　(DomDs).　However,　MDT_RP,　because　of　RecD,　has　rarely　been　explored.　RecD　multivariate　time　series　data　faces　the　challenge　of　changing　information　with　many　noisy　and　abnormal　data　points,　making　it　difficult　for　sequential　methods　(SMs)　to　forecast　MDT_RP　accurately.　To　address　this　gap,　a　novel　smoothing　and　matrix　decomposition　(MD)　based　stacked　bidirectional　gated　recurrent　unit　(STMD_SBiGRU)　is　proposed　for　MDT_RP　forecasting.　Existing　SMs　have　disadvantages　in　that　they　are　highly　affected　by　noisy　data,　which　significantly　affects　their　feature　information　extraction　capability.　The　generated　error　gets　amplified　during　forward　propagation,　thus　interfering　with　the　parameter＇s　optimization.　The　proposed　STMD_SBiGRU　has　the　advantage　of　capturing　the　maximum　variance　in　the　dataset　by　using　various　MD　methods,　as　well　as　reducing　abnormalities　by　applying　various　smoothing　factors.　This　dual　innovation　of　integrating　MD　and　smoothing　facilitates　the　effective　distribution　of　parameters　across　multiple　stacked　layers　and　directions　in　a　proposed　model,　thus　avoiding　complexity　and　overfitting　problems　of　conventional　SMs　while　improving　network　generalization　performance.　The　extensive　experimental　work　demonstrates　that　STMD_SBiGRU　can　forecast　MDT_RP　with　better　performance　and　is　highly　robust　to　noisy　data　compared　to　other　data-driven　methods.