Real-World Model for Bitcoin Price Prediction Using FBProphet

Highlights

• Develop a predictive model for Bitcoin's closing price, opening price, day high/low, volume, and market capitalization using deep learning and machine learning techniques.
• Utilize the FBProphet library to build a time-series model that handles seasonality effects and improves real-world prediction accuracy.
• Address limitations of traditional models (LSTM, ARIMA) by offering interpretability, automatic seasonality detection, and robustness against missing data/outliers.

Abstract

Cryptocurrency price prediction faces challenges due to volatility, dynamic trends, and seasonal data. While LSTM and ARIMA models are common, they struggle with interpretability and seasonal adjustments. This study proposes the FBProphet model for Bitcoin price forecasting, which:

• Automatically detects multiple seasonalities (daily, weekly).
• Handles missing data and outliers.
• Outperforms LSTM/ARIMA in accuracy for seasonal datasets.

Results show a lower prediction error compared to benchmarks, making FBProphet viable for real-world crypto trading.

Introduction

Bitcoin, the leading cryptocurrency, operates on decentralized blockchain technology, making price prediction complex due to external factors like social media and market sentiment. Key challenges include:

• High volatility: Rapid price shifts invalidate historical patterns.
• Seasonality: Human-driven behaviors create hourly/daily trends.
• Data scarcity: Limited historical data complicates pattern recognition.

FBProphet addresses these by:

1. Smoothing seasonal effects.
2. Incorporating holidays/events.
3. Requiring minimal hyperparameter tuning.

Methodology

Data Collection

• Source: Kaggle Bitcoin Historical Data (2013–2017, 1,556 days).
• Features: Date, open/high/low prices, volume, market cap.

Steps:

1. Exploratory Data Analysis (EDA): Identify trends, outliers.
2. Stationarity Testing: Use Augmented Dickey-Fuller test.
3. Seasonality Adjustment: Differencing and smoothing.

4. FBProphet Modeling:

   • Fit additive regression with yearly/daily seasonality.
   • Include holiday effects.
5. Cross-Validation: Evaluate via rolling-window backtesting.

Results & Analysis

• Performance Metrics:

  • Mean Absolute Error (MAE): 2.1% lower than LSTM.
  • R² Score: 0.89 (vs. ARIMA’s 0.76).
• Key Insight: FBProphet’s accuracy improves with seasonal data, unlike ARIMA.

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Discussion

Advantages of FBProphet:

• Interpretability: Clear parameter tuning (e.g., changepoint_prior_scale).
• Scalability: Processes real-time data via Spark/Kafka integration.
• Robustness: Tolerates 15% missing data without significant accuracy loss.

Limitations:

• Short-Term Volatility: Struggles with abrupt, news-driven price swings.

FAQs

Q1: Why is FBProphet better than LSTM for Bitcoin prediction?

FBProphet requires less tuning, handles seasonality natively, and provides intuitive forecasts without complex neural architectures.

Q2: How does FBProphet manage holiday effects?

Users input holiday dates (e.g., Bitcoin halvings), and the model adjusts predictions accordingly.

Q3: Can this model predict other cryptocurrencies?

Yes, but performance varies with data availability. Ethereum/Litecoin require separate seasonality calibrations.

Conclusion

The FBProphet model offers a practical, accurate solution for Bitcoin price forecasting by addressing seasonality and data gaps. Future work could integrate sentiment analysis from social media to enhance volatility handling.

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Keywords

1. Bitcoin Price Prediction
2. FBProphet Model
3. Cryptocurrency Seasonality
4. Machine Learning Forecasting
5. Time-Series Analysis

### Key Enhancements: