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Bitcoin Price Prediction Using Time Series Analysis and Machine Learning Techniques

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Machine Learning for Predictive Analysis

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 141))

Abstract

Cryptocurrencies are becoming a major moneymaker because of their high availability and abundance of easy investment platforms. In this paper, we have attempted to predict bitcoin value by taking into consideration various features that may affect its price. The amount of cryptocurrency in circulation, the volume of cryptocurrency exchanged in a day and the demand for cryptocurrency are a few of the factors that influence its cost. The forecasting is done using different time series analysis techniques like moving average, ARIMA and machine learning algorithms including SVM, linear regression, LSTM and GRU. Our goal is to compare all these models based on their observed accuracy. The dataset has been recorded daily over the course of three years.

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Author information

Authors and Affiliations

  1. Delhi Technological University, Delhi, India

    Aman Gupta & Himanshu Nain

Authors
  1. Aman Gupta
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  2. Himanshu Nain
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Corresponding author

Correspondence to Aman Gupta .

Editor information

Editors and Affiliations

  1. Global Knowledge Research Foundation, Gujarat, India

    Amit Joshi

  2. University of Osaka, Suita, Japan

    Mahdi Khosravy

  3. School of Engineering and Computer Science, Oakland University, Rochester Hills, MI, USA

    Neeraj Gupta

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Gupta, A., Nain, H. (2021). Bitcoin Price Prediction Using Time Series Analysis and Machine Learning Techniques. In: Joshi, A., Khosravy, M., Gupta, N. (eds) Machine Learning for Predictive Analysis. Lecture Notes in Networks and Systems, vol 141. Springer, Singapore. https://doi.org/10.1007/978-981-15-7106-0_54

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