Abstract
In this paper, we use the LSTM version of Recurrent Neural Networks, pricing for Bitcoin. To develop a better understanding of its price influence and a common view of this good invention, we first give a brief overview of Bitcoin again economics. After that, we define the database, including data from stock market indices, sentiment, and . in this investigation, we demonstrate the use of LSTM structures with the series of time mentioned above. In conclusion, we draw the Bitcoin pricing forecast results 30 and 60 days in advance.
Highlights
With the appearance of Bitcoin 10 years ago the globe economist, albeit in small numbers, is lexible and responsive
While it is dif icult to predict the price of Bitcoin, we see that features are critical to the algorithm, future work includes trying out the Gated Recurrent Unit version of Recurrent neural network (RNN), as well as tuning, on existing hyper-parameters
The fact that prices are by a large extent dependent on future prospects rather than historic data
Summary
With the appearance of Bitcoin 10 years ago the globe economist, albeit in small numbers, is lexible and responsive. Bitcoin introduced itself as a program that solved the Double Spend problem Nakamoto and Shah (2017) , a preferred issue with Digital Cash systems. It’s all supported by the thought of ”Bitcoin”. Distributed Ledger Technologies (DLT), Intelligent Agreements, Cryptocurrencies, etc. This was identi ied, during a separate power division mixed with intuitive motive. On the opposite side of the spectrum, and data is taken into account nowadays, over time with a major increase in hardware ef iciency, Machine learning continues to be used. Journal of Engineering Technologies and Management Research, 8(5), 20-28.
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