The phrase "ML models never sleep" is vividly reflected in the 7-day NEAR/USD logarithmic return prediction competition at @AlloraNetwork 🔥 Rankings are updated every hour, and behind the leaderboard are countless algorithms learning, adjusting, and optimizing 24/7. ————————————————————————— ⚡️ Dynamic Adaptation Traditional prediction models are fixed once trained, but Allora allows the model to continuously learn from new data. Price fluctuations of NEAR/USD, changes in market sentiment, on-chain activity data—each signal becomes a data source for model optimization. High-frequency updates mean the model must find a balance between accuracy and response speed. Overfitting historical data can miss new trends, while being too slow can leave the market behind. 🎯 Logarithmic Return Prediction Choosing 7-day logarithmic returns instead of simple price predictions reflects Allora's deep understanding of financial modeling. Logarithmic returns better handle the compound growth characteristics of prices while standardizing volatility across different price levels. This model needs to understand not just the price direction, but also the magnitude of volatility and time decay. ————————————————————————— @AlloraNetwork The best AI does not come from a single model, but from decentralized collaboration among multiple models.