A comparison between ARIMA, LSTM, and GRU for time series forecasting - 文章详情页

作者：

Yamak, Peter T. (Yamak, Peter T..) | Yujian, Li (Yujian, Li.) | Gadosey, Pius K. (Gadosey, Pius K..)

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摘要：

A　critical　area　of　machine　learning　is　Time　Series　forecasting,　as　various　forecasting　problems　contain　a　time　component.　A　series　of　observations　taken　chronologically　in　time　is　known　as　a　Time　Series.　In　this　research,　however,　we　aim　to　compare　three　different　machine　learning　models　in　making　a　time　series　forecast.　We　are　going　to　use　the　Bitcoin＇s　price　dataset　as　our　time　series　data　set　and　make　predictions　accordingly.　The　results　show　that　the　ARIMA　model　gave　better　results　than　the　deep　learning-based　regression　models.　ARIMA　gives　the　best　results　at　2.76%　and　302.53　for　MAPE　and　RMSE　respectively.　The　Gated　Recurrent　Unit　(GRU)　however　performed　better　than　the　Long　Short-term　Memory　(LSTM),　with　3.97%　and　381.34　of　MAPE　and　RMSE　respectively.　©　2019　ACM.

关键词：

Autoregressive moving average model Bitcoin Deep learning Forecasting Learning systems Long short-term memory Regression analysis Time series

作者机构：

[ 1 ] [Yamak, Peter T.]Beijing University of Technology, Beijing, China
[ 2 ] [Yujian, Li]School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, Guangxi, China
[ 3 ] [Gadosey, Pius K.]Beijing University of Technology, Beijing, China

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