Accurately　forecasting　dissolved　gas　concentration　(DGC)　in　transformer　oil　is　crucial　for　ensuring　the　safety　and　reliability　of　power　transformers　and　facilitating　early　anomaly　warning.　Current　methods　for　forecasting　DGC　demonstrate　limited　effectiveness　in　non-stationary　characteristics　with　data-distribution　shifts.　To　address　this,　this　paper　presents　a　novel　adaptive　segmented　temporal　distribution　matching　(AdaSTDM)　model,　consisting　of　the　Toeplitz　inverse　covariance-based　clustering　(TICC)　algorithm　and　time　distribution　matching　(TDM)　algorithm.　To　effectively　adapt　to　the　different　state　distribution　of　the　DGC　data,　the　TICC　algorithm　is　used　to　segment　the　state　domain　of　the　DGC　sequence,　and　the　Jensen-Shannon　(JS)　divergence　is　used　as　an　indicator　to　evaluate　the　segmentation　results.　The　TDM　module　is　designed　to　mitigate　data-distribution　mismatches　by　learning　common　knowledge　among　different　gas　states.　Experimental　results　across　two　real-world　cases　illustrate　that　the　proposed　AdaSTDM　outperforms　various　advanced　methods　in　predicting　both　stationary　and　non-stationary　DGC　data.　(c)　2025　Institute　of　Electrical　Engineers　of　Japan.　Published　by　Wiley　Periodicals　LLC.