Time　series　analysis　is　to　explain　correlation　and　the　main　features　of　the　data　in　chronological　order　by　using　appropriate　statistical　models.　Since　the　past　electricity　generated　sequence　in　China　shows　a　strong　seasonal　variations　and　several　values　for　January　are　lost　in　recent　years,　estimating　the　missing　values　is　an　important　task　before　building　a　model.　This　paper　will　estimate　the　missing　values　by　Holt-Winters　exponential　smoothing　containing　seasonal　components,　and　then　select　appropriate　multiplicative　seasonal　ARIMA　(Integrated　Autoregressive　Moving　Average)　model　to　simulate　the　complementary　electricity　generated　series　and　forecast　the　demand　in　the　next　two　years.　Building　models　and　predicting　the　future　values　for　the　electricity　series　can　not　only　help　estimate　the　development　of　China＇s　electricity　market　but　also　provide　credible　scientific　basis　for　policy　makers　to　formulate　the　electricity　production　indicators.　©　2013　IEEE.