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Explainable Artificial Intelligence Modelling for Bitcoin Price Forecasting

  • Nitendra Kumar* Nitendra Kumar Corresponding author Assistant Professor Amity Business School, Amity University, Noida, India, India ,  
  • Ritesh Dwivedi Ritesh Dwivedi Assistant Professor Symbiosis Institute of Business Management, Noida, India India ,  
  • Padmesh Tripathi Padmesh Tripathi Professor Symbiosis International (Deemed) University, Pune, India India ,  
  • Reeta Mishra Reeta Mishra Scholar Delhi Technical campus, Greater Noida, U.P., India India
Received: May 04, 2025
Accepted: May 20, 2025
Published: June 16, 2025
Volume: 1 (1) | Page: 36-52

Abstract

Precise forecasting of Bitcoin prices has been an essential yet thought-provoking task in the jurisdiction of cryptocurrency trading. Explainable Artificial Intelligence (XAI) has emerged as a widely used tool among researchers across different fields. In this chapter, an XAI Modelling approach for Bitcoin price forecasting, integrating robust methodologies for data analysis and model development, has been proposed. The method starts by importing essential libraries such as matplotlib, pandas, numpy, and scikit-learn, enabling continuous visualisation, data manipulation, and model construction. For comprehensive analysis, historical Bitcoin price data is sourced from trustworthy platforms, and then it is loaded into a structured format. After data loading, the structure and characteristics of the dataset are explored through exploratory data analysis (EDA). To address missing values, techniques such as imputation or removal are used, while for underlying trends, patterns, and seasonality, time-series visualisation methods are used for Bitcoin prices. Descriptive statistics and correlation analysis are employed to further enrich the understanding, offering insights into price distributions and relationships with significant variables. Afterwards, the data goes through the preprocessing to prepare for model training. Min MaxScaler is employed for scaling the features, safeguarding the uniformity across input variables. According to the sequential nature of time series data, the model architecture is designed precisely. For their efficacy in time-series forecasting, Long Short-Term Memory (LSTM) was considered. The model is compiled with an appropriate loss function and optimiser, optimising predictive accuracy during training. Experimentation with hyperparameters further refines the model's performance, enhancing its generalisation ability.

Keywords: Explainable AI, Bitcoin, Price Forecasting, Exploratory Data Analysis, Model Building, Long Short-Term Memory (LSTM).

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