Principle:Shiyu coder Kronos Model Loading

Field	Value
principle_name	Model_Loading
repo	Shiyu_coder_Kronos
domains	Deep_Learning, Time_Series, Autoregressive_Models
last_updated	2026-02-09 14:00 GMT
implemented_by	Implementation:Shiyu_coder_Kronos_Kronos_From_Pretrained

Summary

Loading a pre-trained autoregressive Transformer model that predicts discrete token sequences for financial time series forecasting.

Concept

The Kronos model is a decoder-only Transformer that operates on hierarchical discrete tokens produced by the KronosTokenizer. It predicts future tokens in an autoregressive manner: given a sequence of historical tokens, it generates the next token at each step.

The model uses a two-stage prediction approach:

• Stage 1 (s1): Predict the coarse token using the main Transformer output.
• Stage 2 (s2): Predict the fine token, conditioned on the sampled s1 token via a DependencyAwareLayer.

Loading a pre-trained Kronos model initializes all weights from a checkpoint that has been trained on large-scale financial time series data.

Theory

The Kronos architecture combines several specialized components:

• HierarchicalEmbedding: Fuses s1 (coarse) and s2 (fine) token embeddings into a single representation vector. This enables the model to attend to both levels of the token hierarchy simultaneously.

• TemporalEmbedding: Encodes timestamp features (minute, hour, weekday, day, month) into the model's hidden space, providing temporal context for financial data patterns (e.g., market hours, day-of-week effects).

• DualHead: A two-stage prediction head:
  • First produces s1 logits (coarse token prediction).
  • Then uses cond_forward to produce s2 logits conditioned on the s1 prediction.

• DependencyAwareLayer: Conditions the s2 prediction on the sampled s1 token embedding, ensuring that fine-grained predictions are consistent with the coarse-level structure.

The autoregressive factorization is:

P(tokens) = Product over t: P(s1_t | context) * P(s2_t | s1_t, context)

This hierarchical decomposition reduces the effective vocabulary size at each prediction step compared to predicting a single combined token.

Source

• Repository: Kronos on GitHub
• Decoder-only Transformer architecture inspired by GPT-style language models.

Domains

• Deep_Learning: Transformer-based neural architecture.
• Time_Series: Applied to sequential financial data forecasting.
• Autoregressive_Models: Token-by-token generation with conditional dependencies.

Related Principles

• Principle:Shiyu_coder_Kronos_Tokenizer_Loading - Loading the tokenizer that produces the tokens this model consumes.
• Principle:Shiyu_coder_Kronos_Predictor_Initialization - Wrapping the loaded model and tokenizer into a prediction interface.
• Principle:Shiyu_coder_Kronos_Autoregressive_Token_Generation - The generation loop that uses this model for inference.