Time　series　forecasting　can　provide　more　extensive　data　support　for　good　decision　making.　Recently,　many　deep　learning　based　forecasting　models　have　been　proposed,　and　thus　the　main　problems　are　how　to　learn　effective　historical　information　and　alleviate　the　influence　of　error　propagation,　especially　for　long-term　prediction.　In　this　paper,　we　present　a　new　attention　model　base　on　LSTM　encoderdecoder　architecture　to　predict　long-term　time　series.　We　define　a　similar　scenes　of　time　series,　which　include　periodic　pattern　and　time-nearest　pattern,　and　provide　a　similar　scenes　search　method.　Base　on　this,　we　design　a　hybrid　time　aligned　and　context　attention　model　(HTC-Attn),　and　the　former　focus　on　the　characteristics　of　the　alignment　position,　while　the　latter　focus　on　the　context　features　of　specific　location　in　similar　scenes.　The　attention　gate　is　designed　to　control　the　absorption　degree　of　two　different　types　in　the　prediction　model.　Furthermore,　the　proposed　model　use　double-layer　encoder-decoder　structure　to　learn　the　trend　term　and　time　dependence　of　time　series.　Experimental　results　show　that　HTC-Attn　can　effectively　maintain　long-term　dependence　and　learn　detailed　in　single　factor　time　series　prediction　tasks,　and　accuracy　consistently　outperforms　the　state-of-the-art　baselines　at　least　2%.　(C)　2020　Elsevier　B.V.　All　rights　reserved.