论文标题
使用机器学习技术和公共情感分析对比特币价格实时预测
Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis
论文作者
论文摘要
比特币是第一个数字分散的加密货币，近年来市场资本化显着增加。本文的目的是通过机器学习技术和情感分析确定USD中比特币的可预测价格方向。 Twitter和Reddit引起了研究人员的广泛关注，以研究公共情绪。我们已将情感分析和监督机器学习原理应用于Twitter和Reddit帖子的提取推文，我们分析了比特币价格变动与推文中的情感之间的相关性。我们使用监督学习来开发预测模型并提供对未来市场价格的信息分析，探索了机器学习的几种算法。由于难以评估时间序列（ARIMA）模型的确切性质，因此通常很难产生适当的预测。然后，我们继续使用长期短期记忆细胞（LSTM）实施复发性神经网络（RNN）。因此，我们使用长期记忆（LSTM）技术以更高的效率分析了比特币价格的时间序列模型预测，并将比特币价格的可预测性和比特币推文的情感分析与标准方法（ARIMA）进行了比较。 LSTM的RMSE（根平方误差）为198.448（单特征）和197.515（多功能），而Arima Model RMSE为209.263，它表明具有多功能的LSTM显示了更准确的结果。
Bitcoin is the first digital decentralized cryptocurrency that has shown a significant increase in market capitalization in recent years. The objective of this paper is to determine the predictable price direction of Bitcoin in USD by machine learning techniques and sentiment analysis. Twitter and Reddit have attracted a great deal of attention from researchers to study public sentiment. We have applied sentiment analysis and supervised machine learning principles to the extracted tweets from Twitter and Reddit posts, and we analyze the correlation between bitcoin price movements and sentiments in tweets. We explored several algorithms of machine learning using supervised learning to develop a prediction model and provide informative analysis of future market prices. Due to the difficulty of evaluating the exact nature of a Time Series(ARIMA) model, it is often very difficult to produce appropriate forecasts. Then we continue to implement Recurrent Neural Networks (RNN) with long short-term memory cells (LSTM). Thus, we analyzed the time series model prediction of bitcoin prices with greater efficiency using long short-term memory (LSTM) techniques and compared the predictability of bitcoin price and sentiment analysis of bitcoin tweets to the standard method (ARIMA). The RMSE (Root-mean-square error) of LSTM are 198.448 (single feature) and 197.515 (multi-feature) whereas the ARIMA model RMSE is 209.263 which shows that LSTM with multi feature shows the more accurate result.
