The　water　quality　data　has　missing　values　and　lacks　integrity　because　water　environment　monitoring　equipments　are　easily　damaged　by　environmental　influences,　thereby　affecting　the　analysis　accuracy　of　downstream　tasks.　Traditional　data　imputation　methods　include　mea/Mast　filling,　K-nearest　neighbor,　matrix　factorization,　Lahrangian　interpolation,　etc.,　do　not　consider　time　dependence　or　fail　to　use　complex　relations　among　multiple　features.　Inspired　by　successful　applications　of　various　variants　of　Generative　Adversarial　Networks　(GANs)　on　time　series　data,　this　work　proposes　a　time　series　data　imputation　method　called　GEDA,　which　integrates　-GAN,　an　Encoder-Decoder　structure,　and　an　Autoregressive　network.　GEDA　adopts　GAN　to　learn　the　probability　distribution　of　multi-feature　time　series,　and　imputes　the　missing　values　with　the　generated　data.　Then,　GEDA　combines　feature　extraction　of　the　encoder-decoder　structure,　and　time　dependence　capturing　of　the　autoregressive　network.　Real-life　dataset-based　experimental　results　demonstrate　GEDA　outperforms　several　state-of-the-art　data　imputation　methods　in　terms　of　accuracy.　　©　2022　IEEE.