An Empirical Study on Modeling and Prediction of Bitcoin Prices With Bayesian Neural Networks Based on Blockchain Information
By Huisu Jang, Jaewook Lee. IEEE Access, 2018
411 citations recorded by the source corpus.
Bitcoin has recently attracted considerable attention in the fields of economics, cryptography, and computer science due to its inherent nature of combining encryption technology and monetary units. This paper reveals the effect of Bayesian neural networks (BNNs) by analyzing the time series of Bitcoin process. We also select the most relevant features from Blockchain information that is deeply involved in Bitcoin’s supply and demand and use them to train models to improve the predictive performance of the latest Bitcoin pricing process. We conduct the empirical study that compares the Bayesian neural network with other linear and non-linear benchmark models on modeling and predicting the Bitcoin process. Our empirical studies show that BNN performs well in predicting Bitcoin price time series and explaining the high volatility of the recent Bitcoin price.
Subfields: Economics and Econometrics, Information Systems, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/dcfae19ad20ee57b3f68891d8b21570ab2601613
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In Economics and Econometrics
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In Management Science and Operations Research
Citations in the corpus
- Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques (2019, methodology): “In [18], an experimental study is performed on the estimation and modeling of BTC prices using Bayesian neural network (BNN) based on blockchain information related to BTC supply and”