[论文解读] Real-Time Prediction of BITCOIN Price using Machine Learning Techniques and Public Sentiment Analysis

Raju Sinha, Ali Mohammad Tarif|arXiv (Cornell University)|Jun 18, 2020

Blockchain Technology Applications and Security被引用 36

一句话总结

本论文将 Twitter 和 Reddit 的情感分析与监督学习和 LSTM 时序模型相结合，以预测美元计价的比特币价格，并与 ARIMA 进行比较。结果表明，具有多特征输入的 LSTM 在所测试模型中实现了最佳的 RMSE。

ABSTRACT

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.

研究动机与目标

• 激发使用机器学习和公开情感来研究比特币价格趋势预测。
• 将来自 Twitter 和 Reddit 的情感信号整合到美元计价的比特币价格预测模型中。
• 比较传统时间序列模型与现代神经网络在实时价格预测中的表现。

提出的方法

• 对与比特币相关的 Twitter 和 Reddit 帖子应用情感分析以提取特征。
• 使用监督学习和时间序列模型，包括 LSTM 网络，进行价格预测。
• 以 RMSE 作为指标，将模型与基线 ARIMA 模型进行比较。
• 比较单特征和多特征的 LSTM 配置以评估性能提升。

实验结果

研究问题

• RQ1 来自 Twitter 和 Reddit 的公开情感是否与美元计价的比特币价格波动相关？
• RQ2 在纳入情感特征时，实时比特币价格预测中 LSTM 模型是否优于 ARIMA？
• RQ3 多特征输入对比特币价格预测准确度有何影响？

主要发现

• 具有多特征输入的 LSTM 实现了最佳 RMSE，为 197.515，优于 ARIMA 的 209.263。
• 单特征的 LSTM 的 RMSE 为 198.448，略逊于多特征配置。
• 基线 ARIMA 的 RMSE 为 209.263，说明在此设置下基于 LSTM 的方法具有优势。