KryptoOracle：一个使用Twitter情绪的实时的加密货币价格预测平台
KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments
摘要：
比特币等加密货币正变得越来越受欢迎，已被广泛用作金融交易和资产转移验证等领域的交换媒介。然而，一直缺乏解决方案，可以支持实时的价格预测以应对高汇率波动，处理大量异构数据量，包括社交媒体情绪，同时支持实时的容错和持久性，并提供实时的适应学习算法以应对新的价格和情绪数据。在本文中，我们介绍了KryptoOracle，一种基于Twitter情绪的新型实时的自适应加密货币价格预测平台。集成和模块化平台基于（i）基于Spark的架构，其以持久和容错的方式处理大量的传入数据;（ii）支持情感分析的方法，其可以实时的响应大量的自然语言处理查询;以及（iii）基于在线学习的预测方法，其中模型调整其权重以应对新的价格和情绪。除了提供架构设计，本文还描述了KryptoOracle平台的实现和实验评估。总的来说，拟议的平台可以帮助加快决策，发现新的机会，并根据可用的和越来越大的财务数据量和种类提供更及时的见解。
英文摘要：
Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having been widely used as an exchange medium in areas such as financial transaction and asset transfer verification. However, there has been a lack of solutions that can support real-time price prediction to cope with high currency volatility, handle massive heterogeneous data volumes, including social media sentiments, while supporting fault tolerance and persistence in real time, and provide real-time adaptation of learning algorithms to cope with new price and sentiment data. In this paper we introduce KryptoOracle, a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform is based on (i) a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way; (ii) an approach that supports sentiment analysis which can respond to large amounts of natural language processing queries in real time; and (iii) a predictive method grounded on online learning in which a model adapts its weights to cope with new prices and sentiments. Besides providing an architectural design, the paper also describes the KryptoOracle platform implementation and experimental evaluation. Overall, the proposed platform can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and ever-larger financial data volume and variety.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
