Hybrid Decomposition

The cryptocurrency market's extreme volatility and rapid evolution have long presented substantial challenges for reliable price forecasting. Traditional single-asset models fail to capture the complex interdependencies and heterogeneous nature of digital assets. This groundbreaking research introduces a unified multi-cryptocurrency forecasting architecture that addresses these limitations through an innovative combination of hybrid signal decomposition, sliding window temporal structuring, and comprehensive feature fusion.

Revolutionary Multi-Scale Decomposition Approach

Our framework systematically integrates three powerful decomposition techniques to extract robust market patterns:

1. Empirical Mode Decomposition (EMD)

EMD adaptively decomposes non-linear and non-stationary cryptocurrency price signals into Intrinsic Mode Functions (IMFs), capturing high-to-mid frequency components that reflect short-term market volatility and trading patterns.

2. Discrete Wavelet Transform (DWT)

Using Daubechies-4 wavelets, DWT provides multi-resolution analysis by separating signals into approximation coefficients (low frequency trends) and detail coefficients (high frequency noise), enabling the model to understand both macro trends and micro fluctuations.

3. Seasonal-Trend Decomposition using Loess (STL)

STL isolates trend, seasonal, and residual components using local regression, configured with a 15-day period to capture biweekly patterns common in cryptocurrency markets.

Unprecedented Performance Across Multiple Assets

Comprehensive Feature Engineering Pipeline

The framework employs a sophisticated feature engineering approach that goes beyond traditional price data:

• Technical Indicators: RSI, Bollinger Bands, and MACD calculated independently for each cryptocurrency
• Lag Features: Close price and volume lags at 1, 3, and 7-day intervals capturing autoregressive relationships
• Volatility Measures: Rolling window standard deviations over 7 and 30-day periods
• Temporal Features: Calendar-based effects including day, month, and year components

Model Performance Comparison

Per-Cryptocurrency Performance Excellence

The Random Forest model demonstrates remarkable consistency across diverse cryptocurrency types, from high-cap assets to meme coins:

Cryptocurrency	MAE	RMSE	MAPE (%)
BNB	12.920	17.187	0.021
XRP	0.081	0.127	0.036
TRX	0.009	0.011	0.038
SOL	8.482	10.363	0.062
DOGE	0.013	0.016	0.065

Sliding Window Innovation

A critical innovation in our approach is the implementation of a 90-day sliding window for decomposition. This technique:

• Prevents temporal data leakage by ensuring causality
• Adapts dynamically to evolving market patterns
• Balances historical context with computational efficiency
• Eliminates look-ahead bias that can inflate performance metrics

Key Research Contributions

This research makes several groundbreaking contributions to cryptocurrency forecasting:

1. First Robust Cross-Asset Framework: Establishes the first comprehensive forecasting system capable of handling multiple cryptocurrencies simultaneously
2. Hybrid Decomposition Integration: Pioneers the fusion of EMD, DWT, and STL within a unified framework
3. Temporal Stability: Demonstrates consistent performance across different market regimes with R² consistently above 0.995
4. Scalability: Provides a framework that scales from 8 to potentially hundreds of assets without architecture changes

Temporal Consistency Analysis

Weekly error analysis reveals exceptional temporal stability:

• MAPE drops from 0.13% to 0.04-0.07% after the first prediction week
• R² remains consistently above 0.995 throughout the forecast period
• No major drift or escalating error patterns observed
• Quick recovery from initial volatility demonstrates rapid generalization

Real-World Applications

This framework has immediate practical applications for:

• Algorithmic Trading Systems: High-accuracy predictions enable automated trading strategies with reduced risk
• Portfolio Management: Multi-asset predictions support optimal portfolio allocation decisions
• Risk Assessment: Temporal stability metrics enable real-time risk monitoring
• Market Making: Precise forecasts support liquidity provision strategies

Technical Implementation Details

Data Processing Pipeline

The framework processes daily historical records from the Top 50 cryptocurrencies, covering March 2023 to May 2025. Each cryptocurrency undergoes:

1. Group-wise temporal transformation preserving symbol-specific integrity
2. Z-score normalization for numerical stability
3. One-hot encoding for categorical variables
4. Chronological train-test split maintaining causal ordering

Model Architecture Insights

Random Forest's superior performance stems from:

• Robustness to feature noise through ensemble averaging
• Capability to capture non-linear relationships
• Natural handling of heterogeneous feature types
• No requirement for extensive hyperparameter tuning

Future Research Directions

While our framework achieves exceptional performance, several enhancements could further improve its capabilities:

• Social Sentiment Integration: Incorporating real-time sentiment from social media platforms
• On-chain Metrics: Adding blockchain-specific indicators like transaction volume and active addresses
• Adaptive Decomposition: Dynamic selection of decomposition methods based on market conditions
• Explainability Features: SHAP values for transparent model decisions

Conclusion

This research establishes a new paradigm for cryptocurrency price forecasting through the innovative combination of multi-scale decomposition and unified asset modeling. The framework's exceptional performance across diverse cryptocurrencies, combined with its temporal stability and scalability, makes it a powerful tool for both researchers and practitioners in the digital asset space.

By achieving R² scores of 0.9988 and MAPE below 0.05% across multiple assets simultaneously, this framework demonstrates that accurate, reliable cryptocurrency forecasting at scale is not just possible—it's here.