Create README.md

README.md

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+---
+license: mit
+tags:
+- time-series
+- forecasting
+- finance
+- crypto
+---
+
+# crypto_volatility_forecaster
+
+## Overview
+This model utilizes a Time-Series Transformer architecture to predict the volatility of major cryptocurrencies (e.g., BTC, ETH). By processing historical price action and volume data, it forecasts a probabilistic distribution of future price movements over a 24-hour window based on a 7-day look-back period.
+
+
+
+## Model Architecture
+The model implements a specialized **Encoder-Decoder Transformer** designed for sequential numerical data. 
+- **Temporal Embedding**: Captures hourly and daily seasonalities.
+- **Distribution Head**: Instead of point forecasts, it outputs parameters for a **Student's t-distribution**, which is better suited for the "fat tails" observed in financial market data.
+- **Context Window ($L$):** 168 hours.
+- **Prediction Horizon ($H$):** 24 hours.
+
+The loss function used is the Negative Log-Likelihood ($NLL$):
+$$NLL = -\sum_{t=1}^{H} \log P(x_t | \theta_t)$$
+
+## Intended Use
+- **Risk Management**: Estimating potential Value at Risk (VaR) for digital asset portfolios.
+- **Algorithmic Trading**: Providing volatility signals as features for automated execution strategies.
+- **Financial Research**: Studying market regime shifts and anomaly detection.
+
+## Limitations
+- **Black Swan Events**: Cannot predict volatility spikes caused by external shocks (e.g., regulatory changes, exchange failures) not present in historical price data.
+- **Stationarity**: Financial markets are non-stationary; the model requires frequent retraining to adapt to new market conditions.
+- **Not Financial Advice**: This model is for research purposes and should not be used as the sole basis for investment decisions.