Nowadays, Bitcoin has become the most popular cryptocurrency, which gains the attention of investors and speculators alike. Asset pricing is a risky and challenging activity that enchants lots of shareholders. Indeed, the difficulty in making predictions lies in understanding the multiple factors that affect the Bitcoin price trend. Modeling the market behavior and thus, the sentiment in the Bitcoin ecosystem provides an insight into the predictions of the Bitcoin price. While there are significant studies that investigate the token economics based on the Bitcoin network, limited research has been performed to analyze the network sentiment on the overall Bitcoin price. In this paper, we investigate the predictive power of network sentiments and explore statistical and deep-learning methods to predict Bitcoin future price. In particular, we analyze financial and sentiment features extracted from economic and crowdsourced data respectively, and we show how the sentiment is the most significant factor in predicting Bitcoin market stocks. Next, we compare two models used for Bitcoin time-series predictions: the Auto-Regressive Integrated Moving Average with eXogenous input (ARIMAX) and the Recurrent Neural Network (RNN). We demonstrate that both models achieve optimal results on new predictions, with a mean squared error lower than 0.14%, due to the inclusion of the studied sentiment feature. Besides, since the ARIMAX achieves better predictions than the RNN, we also prove that, with just a linear model, we may obtain outstanding market forecasts in the Bitcoin scenario.

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As an earth‐abundant and environment‐friendly material, the potential of TiO2 in many field, including oxygen evolution reaction (OER), has not yet been fully developed. Here, we fabricate component tunable rutile‐anatase TiO2/reduced graphene oxide (RGO) nanocomposites with high OER activity which exhibit a current density of 10 mA cm−2 at an overpotential of 283 mV. The performance is superior to the previous reported TiO2 (600 mV) and comparable with the leading results of other active materials. The catalytic mechanism is suggested according to our experimental results that the interactions between rutile and anatase, as well as RGO and TiO2 result in the enhancement of adsorption of hydroxyl. Such an adsorption is advantageous to provide a surface reaction environment and reduce the activation energy of the electrochemical process, resulting in the best OER performance. This work should stimulate the design of more materials for the improvement of OER activity.