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Statistical Modeling to Improve Time Series Forecasting Using Machine Learning, Time Series, and Hybrid Models: A Case Study of Bitcoin Price Forecasting

Author

Listed:
  • Moiz Qureshi

    (Government Degree College, Tando Jam, Hyderabad 70060, Pakistan
    Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan)

  • Hasnain Iftikhar

    (Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan)

  • Paulo Canas Rodrigues

    (Department of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil)

  • Mohd Ziaur Rehman

    (Department of Finance, College of Business Administration, King Saud University, P.O. Box 71115, Riyadh 11587, Saudi Arabia)

  • S. A. Atif Salar

    (Al-Barkaat Institute of Management Studies, Aligarh 202122, Dr. A. P. J. Abdul Kalam Technical University, Lucknow 226010, India)

Abstract

Bitcoin (BTC-USD) is a virtual currency that has grown in popularity after its inception in 2008. BTC-USD is an internet communication network that makes using digital money, including digital payments, easy. It offers decentralized clearing of transactions and money supply. This study attempts to accurately anticipate the BTC-USD prices (Close) using data from September 2023 to September 2024, comprising 390 observations. Four machine learning models—Multi-layer Perceptron, Extreme Learning Machine, Neural Network AutoRegression, and Extreme-Gradient Boost—as well as four time series models—Auto-Regressive Integrated Moving Average, Auto-Regressive, Non-Parametric Auto-Regressive, and Simple Exponential Smoothing models—are used to achieve this end. Various hybrid models are then proposed utilizing these models, which are based on simple averaging of these models. The data-splitting technique, commonly used in comparative analysis, splits the data into training and testing data sets. Through comparison testing with training data sets consisting of 30%, 20%, and 10%, the present work demonstrated that the suggested hybrid model outperforms the individual approaches in terms of error metrics, such as the MAE, RMSE, MAPE, SMAPE, and direction accuracy, such as correlation and the MDA of BTC. Furthermore, the DM test is utilized in this study to measure the differences in model performance, and a graphical evaluation of the models is also provided. The practical implication of this study is that financial analysts have a tool (the proposed model) that can yield insightful information about potential investments.

Suggested Citation

  • Moiz Qureshi & Hasnain Iftikhar & Paulo Canas Rodrigues & Mohd Ziaur Rehman & S. A. Atif Salar, 2024. "Statistical Modeling to Improve Time Series Forecasting Using Machine Learning, Time Series, and Hybrid Models: A Case Study of Bitcoin Price Forecasting," Mathematics, MDPI, vol. 12(23), pages 1-15, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3666-:d:1527322
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    References listed on IDEAS

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