<b>Installation and Application of Software Tools for Data Processing and Predictive Modeling in the Chainlink Cryptocurrency Market</b>
Creators: name: Al-Haidari, Hazim
Publisher: name: figshare
Year: 2024
Subjects: classificationCode: 460501
subject: Data engineering and data science

description: The rapid expansion of the cryptocurrency market has prompted significant interest in the analysis and prediction of digital asset prices. Chainlink (LINK), as a decentralized oracle network, plays a pivotal role in the decentralized finance (DeFi) ecosystem by enabling smart contracts to securely interact with external data. The primary objective of this work is to install, practically study, and utilize various software tools necessary for conducting comprehensive data processing and predictive modeling in the context of Chainlink’s historical price data.This work will serve as the foundational basis for a future scientific research paper, which will delve deeper into advanced machine learning techniques for financial forecasting within the cryptocurrency domain.The project is organized into five key steps, each designed to equip the future research with the tools and methodologies required for sophisticated data analysis, model development, and predictive forecasting. The steps are as follows:1. Software Setup: Visual Studio Code (VS Code) will be selected and installed as the primary Integrated Development Environment (IDE), and a Python virtual environment (venv) will be set up. Additionally, essential data processing and visualization libraries such as NumPy, Pandas, Matplotlib, and Seaborn will be utilized. The outcome will be a robust development environment ready for exploratory analysis using these tools.2. Data Set Selection: The second step focuses on identifying and selecting relevant data sets for analysis. Specifically, historical price data for Chainlink will be obtained from TradingView, a widely used platform for financial market data. This data will be described and prepared for subsequent analysis, ensuring it is clean and formatted appropriately for the applied methodologies.3. Program Selection for Data Processing: Specific programs and scripts for data processing will be identified and adapted. These programs will serve as templates for various types of analysis on the selected data.4. Predictive Modeling and Forecasting: Advanced machine learning models will be applied to predict Chainlink's price movements. Models such as linear regression, time series forecasting models (e.g., ARIMA, Prophet), and the Darts library for time series forecasting, including deep learning approaches like LSTM networks, will be utilized. The goal is to train these models on historical price data, evaluate their performance, and generate forecasts to predict future price trends. The insights gained here will be crucial for understanding Chainlink's market dynamics and will significantly enhance the final report's accuracy and depth.5. Report Preparation: The final step involves compiling a report that includes detailed explanations of the methodologies used, visualizations of the results, and a discussion of the findings. This report will provide both numerical and visual insights into Chainlink's price movements, serving as comprehensive documentation of the research process and its outcomes.