Kronos

Compare computational requirements for Kronos-base vs Kronos-mini inference discover hardware specs and performance benchmarks. Kronos-mini offers faster edge deployment.

Master Kronos fine-tuning with our configuration guide. Discover key hyperparameters like lookback window, predict window, epochs, and learning rates for custom datasets.

Learn to fine-tune the Kronos tokenizer for new market data. This guide details preparing CSV data, configuring YAML, and running the finetune script to adapt the Binary Spherical Quantizer.

Learn how Kronos uses sample_count to average stochastic trajectories, reducing variance for stable and accurate time series forecasts.

Learn how Kronos normalizes and denormalizes price data for accurate predictions. Understand z-score transformation for time series forecasting with its per-series mean and standard deviation approach.

Understand the role of BSQuantizer entropy penalty terms gamma0, gamma, and zeta in Kronos codebook learning. Control codebook diversity by regularizing entropy and overall strength.

Learn how Kronos computes loss with DualHead for s1 and s2 token prediction by averaging cross-entropy terms and optionally masking padded positions for accurate gradient updates.

Master Kronos batch prediction consistency with predict_batch(). Ensure identical series lengths, horizons, and OHLCV columns for accurate results. Learn the complete requirements.

Master memory management in Kronos for long predictions with auto_regressive_inference. Learn how sliding windows ensure O(max_context) complexity, not prediction length. Optimize your time-series analysis.

Learn about the clip parameter in KronosPredictor. Understand its default value of 5 and how it prevents extreme outliers from destabilizing inference.

Kronos TemporalEmbedding skillfully encodes time by breaking timestamps into discrete calendar components, embedding each, and summing them for a unified temporal vector to enhance model embeddings.

Discover Kronos' novel hybrid hierarchical-binary quantization strategy for K-line data. Learn how it transforms OHLCV time series into discrete tokens for efficient analysis.