Shoriful025/crypto_volatility_prediction_informer

cryptovolatilityprediction_informer

Overview

This model implements the Informer architecture for long-sequence time-series forecasting. It is specifically tuned to predict Bitcoin (BTC) and Ethereum (ETH) price volatility over a 24-hour horizon based on a rolling 7-day window of hourly OHLCV data.

Model Architecture

The Informer model addresses the $O(L^2)$ complexity of standard Transformers using:

• —ProbSparse Self-Attention: Reduces complexity to $O(L \log L)$.
• —Self-attention Distilling: Highlights dominant features across temporal dimensions.
• —Generative Decoder: Predicts long-term sequences in one forward step to prevent error accumulation.

Intended Use

• —Risk Management: Estimating potential volatility spikes for automated trading desks.
• —Portfolio Hedging: Generating signals for derivative positioning.
• —Market Research: Analyzing the temporal dependencies of crypto-asset fluctuations.

Limitations

• —Black Swan Events: The model cannot predict volatility caused by external regulatory news or exchange failures not reflected in historical price patterns.
• —Non-Stationarity: Crypto markets are highly non-stationary; the model requires frequent re-training (e.g., every 48 hours).
• —Financial Risk: This tool is for informational purposes and is not financial advice.