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首页> 外文会议>International Conference on Reliability, Infocom Technologies and Optimization >Predicting Fluctuations in Cryptocurrencies' Price using users' Comments and Real-time Prices
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Predicting Fluctuations in Cryptocurrencies' Price using users' Comments and Real-time Prices

机译：使用用户的评论和实时价格预测加密货币价格的波动

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This paper shows the prediction of fluctuation in the future price of cryptocurrencies. Users' comments and tweets from twitter using Apache Flume and Price data was fetched from exchanges. Bitcoin first documented by allies Satoshi Nakamoto, the first decentralized currency payment system has gained a considerable attention in the financial system, economics, social media and computer science due to its combination of peer-to-peer nature, encryption technology, and monetary unit. Predicting the price of Bitcoin and other cryptocurrencies is a great challenge because it is immensely complex and dynamic in nature. In this paper, we have tried to predict the future price of cryptocurrencies like Bitcoin using LSTM (Long Short-Term Memory) and used Twitter data to predict public mood. By combining both market sentiment and social sentiment because bitcoin price shows mixed properties. We also have selected some other important features from the blockchain information which has a major impact on Bitcoin's supply and demand and using them to train model that improves the predictive power of the future Bitcoin price. We have performed a deep study of how data from social media affect the price of Bitcoin and so we have included the twitter data in model training. Our model shows that how well LSTM predict the price of Bitcoin considering the high volatility. The precision given by our model is 60% and accuracy is 50%. More focus is not given to accuracy, in this case, considering the highly volatile market.
机译：本文显示了在未来加密货币价格下的波动预测。使用Apache Flume和价格数据的推特的用户的评论和推文是从交换中获取的。比特币首先由盟友萨米·纳卡莫托举报，由于其对等性质，加密技术和货币单位的结合，第一次分散货币支付系统在金融体系，经济学，社交媒体和计算机科学中取得了相当大的关注。预测比特币和其他加密货币的价格是一个巨大的挑战，因为它在自然界中非常复杂和动态。在本文中，我们试图使用LSTM（长短期内存）并使用Twitter数据来预测比特币等加密货币的未来价格来预测公共情绪。通过结合市场情绪和社会情绪，因为比特币价格显示混合特性。我们还从区块链信息中选择了一些其他重要特征，这对比特币的供需产生了重大影响，并使用它们培训模型，从而提高了未来比特币价格的预测力量。我们对社交媒体数据的深入研究了影响比特币的价格，因此我们已经在模型培训中包含了Twitter数据。我们的模特表明，LSTM如何预测考虑到高波动性的比特币价格。我们模型给出的精度为60％，准确性为50％。考虑到高度挥发性市场，在这种情况下，不赋予准确性更多的重点。

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