Chronos

As explained in Time Series Forecasting Using Foundation Models

The Chronos framework adapts language models into pretrained probabilistic forecasting models by transforming time-series data into a discrete representation.

Chronos is a framework for adapting language models into pretrained probabilistic forecasting models. It mean-scales and quantizes continuous time-series values into a fixed vocabulary, then reverses that process to produce forecasts. section 5.0 section 5.2

The resulting model performs regression through classification: it predicts future value tokens with a language-model objective rather than minimizing MAE or MSE. Existing language-model architectures therefore remain unchanged. section 5.4

Figure 5.2 Overview of the Chronos framework, which adapts a language model for time-series forecasting. First, the time series is tokenized, which involves mean scaling and quantization. Then the output can be used to train the language model.

Follow the pipeline from mean scaling and quantization to language-model prediction, then dequantization and unscaling. section 5.2

Practical characteristics shape how Chronos should be applied:

• Uses pretrained T5-based models in several sizes, with hardware determining which model is practical. section 5.4.2 section 5.9
• Installs as a Python package and supports zero-shot forecasting, although custom functions may still be needed for streamlined workflows. section 5.5 section 5.9
• Works best on horizons shorter than 64 time steps; longer forecasts may lose quality, and exogenous features are unsupported. section 5.10

Forecasting and anomaly detection

For weekly Walmart store sales, Chronos can generate probabilistic forecasts and be evaluated with cross-validation using MAE and sMAPE. For New York City taxi rides, a 99% prediction interval flags values outside its bounds as anomalies. That procedure found all labeled anomalies but produced many false positives, with 21% precision in the reported comparison. section 5.7.5 section 5.8

How Time Series Forecasting Using Foundation Models covers it

How deep it goes

Explains tokenization, classification-based training, package use, evaluation, fine-tuning, and operational limitations.

Examples

Uses Walmart weekly sales, New York City taxi rides, and daily blog visits to demonstrate forecasting and anomaly detection.

What it assumes

Assumes basic Python and time-series handling while demonstrating tensor formatting, model initialization, prediction, and evaluation setup.

What you can do after

Apply Chronos via its Python package, generate probabilistic forecasts, evaluate them with cross-validation, and construct interval-based anomaly detectors.

How it connects

Connects language models with probabilistic forecasting, time-series tokenization, fine-tuning, cross-validation, and anomaly detection.

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Related concepts

• data framecontrasts with · A data frame is a two-dimensional tabular data structure with labeled rows and columns that can contain values of different types.
• language modeluses · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• tensoruses · A tensor is a multidimensional, array-like structure of values indexed along zero or more dimensions and used for numerical computation.
• foundational modelkind of · A base or foundation model is a pretrained model that provides learned parameters or capabilities as a starting point for reuse or adaptation to downstream tasks.

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