Enhancing Financial Predictions Based on Bitcoin Prices using Big Data and Deep Learning Approach
Abstract
An increasingly popular alternative investing strategy, trading digital money is gaining traction daily. In terms of technological implementation, Bitcoin is among the most prominent digital currencies. Bitcoin is decentralised and doesn't answer to any government, but that hasn't stopped many investors from trading in it and stimulating the economy. The purpose of this study is to forecast the next day's bitcoin price using five separate statistical and ML methods and to evaluate and contrast them. The ever-changing cryptocurrency industry, however, makes Bitcoin price prediction an increasingly important task. This study examines the effectiveness of several MLP, RNN, ARIMA, and SVM-based models in predicting Bitcoin prices. When applied to the historical price data, an MLP model turned out to be the most efficient, with an R² at 95.9%, while ARIMA was at 90.31%, SVM at 67.3%, and RNN at only 50.25%. The 60-day evaluation proved the proposed MLP model’s accuracy in capturing short-term price movements, thus supporting the concept of a good fit. This work is then useful to establish sound guidelines in including ML and DL strategies in financial prediction, showcasing MLP model to improve decision-making during turbulence. More improvements can include other market factors and better configurations for improved precision and capacity.
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