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Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks.

Bhaskar Tripathi1, Rakesh Kumar Sharma1

  • 1School of Humanities and Social Sciences, Thapar Institute of Engineering and Technology, Patiala, 147004 India.

Computational Economics
|November 7, 2022
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Summary
This summary is machine-generated.

Accurate Bitcoin price forecasting is challenging due to its volatility. This study developed a framework using technical indicators and deep neural networks, achieving high accuracy for short-term Bitcoin price prediction.

Keywords:
Bayesian optimizationDeep learningOutlier detectionSavitzky–Golay FilterTime series forecasting

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Area of Science:

  • * Financial Technology and Computational Finance
  • * Machine Learning and Deep Learning Applications

Background:

  • * Bitcoin, a decentralized digital currency, exhibits significant price volatility, making accurate forecasting crucial for investors.
  • * Existing Bitcoin price prediction models face challenges due to data noise, feature selection complexity, and inherent market unpredictability.

Purpose of the Study:

  • * To develop a robust Bitcoin price forecasting framework addressing data noise and optimizing feature selection.
  • * To evaluate the predictive power of fundamental indicators, technical indicators, and lagged prices using deep learning models.
  • * To enhance short-term Bitcoin price prediction accuracy and reliability for traders and researchers.

Main Methods:

  • * A three-step hybrid feature selection procedure was employed to identify key predictive variables.
  • * Hampel and Savitzky-Golay filters were utilized for outlier imputation and noise reduction in Bitcoin time series data.
  • * Deep neural networks, optimized via Bayesian Optimization, were used for forecasting prices at 1, 3, 5, and 7-day intervals.

Main Results:

  • * The Deep Artificial Neural Network model, utilizing technical indicators, demonstrated superior performance over benchmark models (LSTM, BiLSTM, CNN-BiLSTM).
  • * The proposed framework achieved high accuracy, with an Absolute Percentage Error (APE) as low as 0.28% for next-day forecasts and 2.25% for 7-day forecasts.
  • * The model significantly outperformed existing literature benchmarks on out-of-sample data from January 1, 2021, to November 1, 2021.

Conclusions:

  • * The developed forecasting framework effectively reduces noise and selects optimal features for improved Bitcoin price prediction.
  • * Technical indicators, when processed through advanced deep learning models, offer significant predictive power for short-term Bitcoin price movements.
  • * This research provides valuable contributions to feature selection, data preprocessing, and hybrid deep learning models for financial time series analysis.