This　study　proposes　a　mobile　positioning　method　that　adopts　recurrent　neural　network　algorithms　to　analyze　the　received　signal　strength　indications　from　heterogeneous　networks　(e.g.,　cellular　networks　and　Wi-Fi　networks)　for　estimating　the　locations　of　mobile　stations.　The　recurrent　neural　networks　with　multiple　consecutive　timestamps　can　be　applied　to　extract　the　features　of　time　series　data　for　the　improvement　of　location　estimation.　In　practical　experimental　environments,　there　are　4525　records,　59　different　base　stations,　and　582　different　Wi-Fi　access　points　detected　in　Fuzhou　University　in　China.　The　lower　location　errors　can　be　obtained　by　the　recurrent　neural　networks　with　multiple　consecutive　timestamps　(e.g.,　two　timestamps　and　three　timestamps);　from　the　experimental　results,　it　can　be　observed　that　the　average　error　of　location　estimation　was　9.19　m　by　the　proposed　mobile　positioning　method　with　two　timestamps.　©　2019　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.