The　regularly　issued　low　frequency　data,　such　as　the　change　of　fund　position　(weekly),　and　Producer　Price　Index　(monthly),　can　affect　the　subsequent　trend　of　stock　returns.　However,　the　forecasting　effect　of　low　frequency　data　on　high　frequency　has　not　been　discussed　amply.　This　paper　proposes　a　new　mixed　frequency　neural　network　that　helps　to　fill　this　research　gap.　The　original　time　series　is　decomposed　into　several　components　through　ensemble　empirical　mode　decomposition,　then　the　frequency　alignment　method　is　applied　to　integrate　the　high　frequency　component　with　low　frequency　variable　as　inputs,　and　the　CNN-BiLSTM-Attention　network　completes　the　remaining　forecasting　work.　The　empirical　results　show　that　compared　with　other　benchmark　models,　the　proposed　procedures　perform　better　when　predicting　the　high　frequency　components　and　obtain　a　smaller　statistical　error　in　the　final　ensemble　results.　The　proposed　model　has　great　potential　for　the　forecasting　of　reverse　mixed　time　series.　©　2022　-　IOS　Press.　All　rights　reserved.