  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.
