Efficient Bitcoin Price Forecasting Using Deep Learning and Ensemble Methods

Abstract

Bitcoin has emerged as one of the most volatile and widely traded digital assets, making accurate price forecasting both challenging and essential for investors and researchers. The aim of this project is to design and implement a forecasting system that predicts Bitcoin prices using a combination of machine learning and time-series automation techniques. The system is built using the AutoTS library, which automatically evaluates multiple forecasting models and generates a 7-day price forecast. To enhance interpretability, a Random Forest regressor is integrated to identify the relative importance of technical indicators such as daily returns, moving averages, volatility, and volume change. Together, these models provide both predictive accuracy and insights into market behavior. The application has been developed as a Tkinter-based desktop interface, allowing users to input custom date ranges, fetch historical data directly from the Binance API, and visualize forecasts alongside actual price trends. The system also supports exporting results into CSV and Excel formats for further analysis. Evaluation metrics including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used to assess forecast quality. This work demonstrates the potential of combining automated time-series modeling with ensemble learning to capture complex financial patterns. While the system performs effectively for short-term forecasting, its scope can be extended in the future with deep learning models, additional data sources, and cloud-based deployment.

Introduction

The explosive growth of cryptocurrencies, especially Bitcoin, has driven demand for accurate price forecasting due to its extreme volatility. Traditional models like ARIMA are limited in capturing Bitcoin’s complex patterns, while deep learning models like LSTM, though powerful, are resource-intensive. To address this, a hybrid forecasting system was developed combining:

• AutoTS (automated time-series modeling) for efficient and accurate 7-day forecasts.

• Random Forest Regressor for feature importance analysis and interpretability.

This system uses live data from the Binance API and presents results through a Tkinter GUI.

Key Features of the Proposed System

1. Data Acquisition

• Fetches real-time OHLCV (Open, High, Low, Close, Volume) data from Binance.

• Automates data formatting using Pandas.

2. Preprocessing & Feature Engineering

• Calculates indicators like:

  • Returns

  • Moving Averages (MA7, MA21)

  • Volatility

  • Volume Change

• These features are used to enhance model performance and explainability.

3. Forecasting Module

• Uses AutoTS to automate model selection and generate 7-day forecasts.

• Configuration: forecast_length=7, model_list='superfast', suitable for academic or real-time use.

4. Feature Importance Analysis

• Uses RandomForestRegressor to rank features affecting Bitcoin prices.

• Enhances model transparency by showing which indicators influence the prediction.

5. Evaluation Metrics

• Uses:

  • MAE (Mean Absolute Error)

  • RMSE (Root Mean Square Error)

  • MAPE (Mean Absolute Percentage Error)

• Provides quantitative performance assessment.

6. User Interface (GUI)

• Built using Tkinter.

• Allows users to:

  • Input date ranges.

  • Trigger forecast generation.

  • View historical and predicted prices in graphs.

  • Analyze feature importance.

  • Export results in CSV or Excel format.

Literature Review Highlights

Previous works have used:

• Machine Learning models (Random Forest, SVM, ANN, Naive Bayes)

• Deep Learning models (LSTM, GRU, CNN)

• Hybrid Models (LSTM-GRU, ARIMA + ML, Ensemble LSTM)

Findings:

• Hybrid and ensemble models outperform individual statistical or ML models.

• Random Forest shows strong performance with continuous data.

• LSTM-based architectures are especially effective under high market volatility.

Technical Stack

• Pandas: Time-series data handling.

• NumPy: Numerical computations.

• Matplotlib: Forecast and feature importance visualization.

• scikit-learn: ML model (Random Forest) and evaluation metrics.

• AutoTS: Time-series forecasting automation.

• python-binance: Real-time crypto data retrieval.

• Tkinter: GUI development.

Results & Performance

A. Forecast Accuracy

• Visual and tabular forecast displays.

• Accurate short-term (7-day) predictions.

• Forecast errors remain within acceptable limits.

B. Interpretability

• Feature importance ranked by Random Forest.

• Users gain insights into which indicators (e.g., MA21, volatility) drive predictions.

C. Usability

• No coding required.

• Accessible interface suitable for traders, analysts, and researchers.

Conclusion

The project “Bitcoin Price Forecasting using AutoTS and Machine Learning” successfully demonstrates how historical data and machine learning techniques can be combined to predict Bitcoin prices. The system integrates automated forecasting with AutoTS, feature importance analysis with RandomForest, and presents results through a simple graphical interface. The outcomes show that the framework is reliable, user-friendly, and capable of producing short-term forecasts with evaluation metrics for accuracy. This project meets its objective of building an efficient forecasting tool and provides a strong foundation for future research and real-world applications in cryptocurrency analysis.

References

[1] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008. [2] M. Briere, K. Oosterlinck, and A. Szafarz, “Virtual currency, tangible ` return: Portfolio diversification with bitcoins,” Tangible Return: Portfolio Diversification with Bitcoins (September 12, 2013), 2013. [3] Kaastra and M. Boyd, “Designing a neural network for forecasting financial and economic time series,” Neurocomputing, vol. 10, no. 3, pp. 215–236, 1996. [4] H. White, “Economic prediction using neural networks: The case of ibm daily stock returns,” in Neural Networks, 1988., IEEE International Conference on. IEEE, 1988, pp. 451–458. [5] C. Chatfield and M. Yar, “Holt-winters forecasting: some practical issues,” The Statistician, pp. 129–140, 1988. [6] L. Breiman, Random Forests, Machine Learning, 2001 — foundational Random Forest algorithm [7] S. Hochreiter & J. Schmidhuber, Long Short-Term Memory, Neural Computation, 1997 — original LSTM paper [8] T. Fischer & C. Krauss, Deep learning with long short-term memory networks for financial market predictions, EJOR, 2018 [9] R. J. Hyndman & A. B. Koehler, Another look at measures of forecast accuracy, Int. J. Forecasting, 2006 — explains forecast error metrics (MAPE, MASE, etc.) [10] S. J. Taylor & B. Letham, Forecasting at Scale (Prophet), 2017/2018 — Prophet forecasting approach [11] M. Feurer et al., Auto-sklearn: Efficient and Robust Automated Machine Learning [12] R. J. Hyndman & G. Athanasopoulos, Forecasting: Principles and Practice (textbook/online) — excellent practical guide & benchmarks for forecasting workflows. [13] G. Zhang, B. E. Patuwo & M. Y. Hu, Forecasting with artificial neural networks: The state of the art, Int. J. Forecasting, 1998 — classic survey on ANN forecasting methods. [14] O. B. Sezer, M. U. Gudelek & A. M. Ozbayoglu, Financial time series forecasting with deep learning: a systematic literature review, Applied Soft Computing, 2020 — recent survey of DL models and pitfalls.

Copyright

Copyright © 2025 Samalla Rahul. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.