In　order　to　solve　the　problem　that　the　poor　prediction　accuracy　of　traditional　model　for　the　time　series　with　missing　data,　the　strong　time　series　modeling　ability　of　long　short-term　memory　(LSTM)　neural　network　is　used　and　the　long　short-term　memory　neural　network　with　time　gate　(TG-LSTM)　is　proposed.　Firstly,　the　TG-LSTM　unit　structure　is　proposed,　which　can　be　used　to　realize　the　online　estimation　of　network　inputs　and　real-time　prediction　outputs,　simultaneously.　Secondly,　the　forward　propagation　algorithm　is　designed　according　to　the　TG-LSTM　structure　to　realize　the　synchronization　of　input　filling　and　output　prediction.　Furthermore,　the　learning　algorithm　of　TG-LSTM　neural　network　is　established　to　uniformly　train　input　filling　and　output　prediction　tasks.　Finally,　the　experimental　results　of　the　Mackey-glass　benchmark　data　set,　monthly　average　temperature　data　set　and　the　ammonia　nitrogen　concentration　prediction　of　waste　water　treatment　process　show　that　compared　with　the　traditional　methods,　the　incomplete　time　series　can　be　filled　and　predicted　by　the　TG-LSTM　neural　network　model　with　higher　accuracy.　©　2022,　Editorial　Department　of　Control　Theory　&　Applications.　All　right　reserved.