Causal AI reorients your approach from static prediction to rolling recalibration. Its strength isn't just in making predictions; it emphasizes real-time adjustments based on current data, improving responsiveness to market changes.
The transformer architecture can efficiently learn representations for time series data by treating time series sub-sequences as tokens, significantly simplifying the modeling process.