Forecasting Bitcoin price using time opinion mining and bi-directional GRU

【Author】 Akbar, Sumaiya Begum; Thanupillai, Kalaiselvi; Govindarajan, Valarmathi

【Source】JOURNAL OF INTELLIGENT & FUZZY SYSTEMS

【影响因子】1.737

【Abstract】Bitcoin is an innovative decentralized digital currency without intermediaries. Bitcoin price prediction is a demanding need in the present situation. This paper makes an investigation on the Bitcoin price forecast with a Bi-directional Gated Recurrent Unit (GRU) time series method, combined with opinion mining based on Twitter and Reddit feeds. An hourly basis sentimental analysis through the implementation of Natural Language Processing presents a positive impact of sentimental analysis on the Bitcoin price prediction. For prediction, RNN, long-short memory, GRU has been utilized. Unidirectional and Bi-directional versions of all three networks with and without sentimental analysis were implemented for comparison. Of all the techniques implemented Bi-directional GRU along with sentimental analysis gives a minimum RMSE and Minimum absolute percentage error of 1108.33 and 7.384%. Thus, the framework including Bi-Directional GRU along with Sentimental Analysis provides better results than the State-of-art methods.

【Keywords】Bitcoin; neural network; mining; GRU; RMSE; MAPE

【发表时间】2022

【收录时间】2022-02-18

【文献类型】期刊

【主题类别】

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