TimeGPT Foundation Forecasting

TimeGPT is a transformer based foundation model trained on massive multi-domain time series corpora. It learns general patterns like seasonality, shocks, and regime drift, then adapts quickly to specific crypto symbols with a short context window.

Transformer Attention Multi Horizon Foundation Priors Probabilistic Output

TimeGPT Engine

Global context, local adaptation, and fast decoding.

Context embedding to probabilistic forecast pipeline.

How TimeGPT Works

TimeGPT transforms past observations into embeddings, applies multi-head attention to capture long range dependencies, and predicts a distribution of future values. The model is trained on many markets, so it recognizes patterns such as volatility clustering and sudden liquidity shifts before fine tuning on a target symbol.

For crypto, we feed TimeGPT with price, volume, funding rates, and calendar signals, then project multiple horizons with uncertainty bands. That uncertainty becomes a confidence weight in the trading decision engine.

Key Technical Concepts

Global pretraining for cross-market priors.
Positional encodings for irregular time spacing.
Quantile outputs for asymmetric risk modeling.
Fast autoregressive decoding for real time updates.

Embed Context

Normalize price history, inject time features, and map into a shared latent space.

Attend Globally

Multi-head attention weights past segments based on relevance to the current regime.

Decode Horizon

Generate multiple step forecasts with probabilistic intervals and calibration checks.

Score Confidence

Translate uncertainty into a confidence signal for downstream risk sizing.

Core Equations

Attention based forecasting enables long range memory with scalable computation while keeping uncertainty front and center.

Attention

Attention(Q, K, V) = softmax(QK^T / sqrt(d_k)) V

Weights the most relevant context for each forecast step.

Sequence model

p(y_{t+1:t+h} | y_{1:t}, x_{1:t+h}) = f_theta(.)

Learns conditional distributions over future horizons.

Quantile loss

L_q(y, y_hat) = max(q(y - y_hat), (q - 1)(y - y_hat))

Targets asymmetric risk for downside and upside.

Where TimeGPT Fits

Strengths

Captures long range dependencies and irregular cycles.
Multi horizon forecasts with calibrated uncertainty.
Strong transfer learning across correlated assets.

Limits

Requires careful normalization to avoid drift bias.
Expensive to train compared to classical baselines.
Attention can overfit short windows without gating.

TimeGPT is the long memory engine that powers mid horizon projections and signal validation.