Providing　interpretable　explanations　can　notably　enhance　users＇　confidence　and　satisfaction　with　regard　to　recommender　systems.　Counterfactual　explanations　demonstrate　remarkable　performance　in　the　realm　of　explainable　sequential　recommendation.　However,　current　counterfactual　explanation　models　designed　for　sequential　recommendation　overlook　the　temporal　dependencies　in　a　user＇s　past　behavior　sequence.　Furthermore,　counterfactual　histories　should　be　as　similar　to　the　real　history　as　possible　to　avoid　conflicting　with　the　user＇s　genuine　behavioral　preferences.　This　paper　presents　counterfactual　explanations　by　Considering　temporal　dependencies　(CETD),　a　counterfactual　explanation　model　that　utilizes　a　variational　autoencoder　(VAE)　for　sequential　recommendation　and　takes　into　account　temporal　dependencies.　To　improve　explainability,　CETD　employs　a　recurrent　neural　network　(RNN)　when　generating　counterfactual　histories,　thereby　capturing　both　the　user＇s　long-term　preferences　and　short-term　behavior　in　their　real　behavioral　history.　Meanwhile,　CETD　fits　the　distribution　of　reconstructed　data　(i.e.,　the　counterfactual　sequences　generated　by　VAE　perturbation)　in　a　latent　space,　and　leverages　learned　variance　to　decrease　the　proximity　of　counterfactual　histories　by　minimizing　the　distance　between　the　counterfactual　sequences　and　the　original　sequence.　Thorough　experiments　conducted　on　two　real-world　datasets　demonstrate　that　the　proposed　CETD　consistently　surpasses　current　state-of-the-art　methods.