The　presence　of　missing　values　is　a　pervasive　and　unavoidable　phenomenon　in　sensor　data.　Despite　numerous　efforts　from　researchers　to　address　this　issue　through　imputation　techniques,　particularly　in　deep　learning　models,　the　unique　data　distributions　and　periods　inherent　in　real-world　sensor　data　are　often　neglected.　This　paper　presents　a　novel,　multistage　deep　learning-based　imputation　framework　with　adaptability　to　missing　value　imputation.　The　framework　incorporates　a　mixture　measurement　index　that　accounts　for　both　low-and　higher-order　statistical　aspects　of　data　distribution　and　a　more　adaptive　evaluation　metric,　which　improves　upon　traditional　mean　squared　error.　Additionally,　a　multistage　imputation　strategy　and　dynamic　data　length　adjustment　are　integrated　into　the　imputation　process　to　account　for　variations　in　data　periods.　Empirical　results　on　diverse　sensor　data　demonstrate　the　superiority　of　the　proposed　framework,　particularly　in　addressing　large　segment　imputation　issues,　as　evidenced　by　improved　imputation　performance.　The　implementation　and　experimental　results　have　been　made　publicly　available　on　GitHub.　1　&　COPY;　2023　Published　by　Elsevier　B.V.