Time　series　anomaly　detection　through　unsupervised　methods　has　been　an　active　research　area　in　recent　years　due　to　its　enormous　potential　for　networks　management.　The　representation　and　reconstruction　of　time　series　have　made　extraordinary　progress　in　existing　works.　However,　time　series　is　known　to　be　complex　in　terms　of　their　temporal　dependency　and　stochasticity,　which　makes　anomaly　detection　difficult.　To　this　end,　we　propose　a　novel　approach　based　on　a　decomposition　auto-transformer　networks(DATN)　for　time　series　anomaly　detection.　The　time　series　is　decomposed　into　seasonal　and　trend　components,　and　renovated　as　a　basic　inner　block　deep　model.　With　this　design,　transformers　can　decompose　complex　time　series　in　a　progressive　manner.　We　also　design　an　auto-transfomer　block　that　determines　dependencies　and　representation　aggregation　at　the　sub-series　level　based　on　series　seasonal　and　trend　components.　Moreover,　the　complex　transformer　decoder　is　replaced　by　a　simple　linear　decoder,　which　makes　the　model　more　efficient.　Extensive　experiments　on　various　public　benchmarks　demonstrate　that　our　method　has　achieved　state-of-the-art　performance.