---
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.