  Since the birth of Bitcoin in 2009, cryptocurrencies have emerged to become a
global phenomenon and an important decentralized financial asset. Due to this
decentralization, the value of these digital currencies against fiat currencies
is highly volatile over time. Therefore, forecasting the crypto-fiat currency
exchange rate is an extremely challenging task. For reliable forecasting, this
paper proposes a multimodal AdaBoost-LSTM ensemble approach that employs all
modalities which derive price fluctuation such as social media sentiments,
search volumes, blockchain information, and trading data. To better support
investment decision making, the approach forecasts also the fluctuation
distribution. The conducted extensive experiments demonstrated the
effectiveness of relying on multimodalities instead of only trading data.
Further experiments demonstrate the outperformance of the proposed approach
compared to existing tools and methods with a 19.29% improvement.
