Statistical and Computational Methods for Bitcoin Price Forecasting

SOWMYA B; SNEHA T J; GEETHA M M

Statistical and Computational Methods for Bitcoin Price Forecasting

Authors: SOWMYA B, SNEHA T J, GEETHA M M

Unique Paper ID: 170047
Volume: 6
Issue: 5
PageNo: 26-33

Keywords: Patrolling IOT Raspberry pi module Noob software

Abstract:
Cryptocurrency, particularly Bitcoin, has gained immense popularity among investors due to its decentralized nature and blockchain-based technology. This research aims to predict Bitcoin prices by analyzing various factors that influence its value. The investigation is divided into two phases. The first phase focuses on identifying daily market trends and extracting optimal features related to Bitcoin's price movements. The dataset includes daily records of Bitcoin’s payment network metrics, trading volumes, and external factors like stock market indices and sentiment analysis. Understanding these trends will provide a comprehensive insight into the factors driving Bitcoin's value. In the second phase, machine learning algorithms, particularly Long Short-Term Memory (LSTM) networks, are employed to predict the direction of daily price changes with high accuracy. LSTM's capability to analyse time-series data makes it well-suited for capturing the temporal dependencies in cryptocurrency markets. The inclusion of diverse data sources such as blockchain analytics, market sentiment, and coin tracking platforms enhances the predictive model's robustness. Bitcoin’s value is unique as it does not depend on traditional economic events or government policies, making it highly volatile and unpredictable. Machine learning methods simplify this complexity by modeling nonlinear relationships within the data. By integrating advanced computational techniques and statistical analysis, this study offers a framework for reliable price forecasting, addressing the challenges faced by investors in navigating the dynamic cryptocurrency market. This research not only contributes to the financial literature but also supports decision-making for both novice and seasoned investors.

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ISSN: 2349-6002
Volume: 6
Issue: 5
PageNo: 26-33

Statistical and Computational Methods for Bitcoin Price Forecasting

Available:https://ijirt.org/Article?manuscript=170047

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