{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Chronos provides pretrained transformer-based models for zero-shot time series forecasting, supporting univariate, multivariate, and covariate-informed tasks with quantile-based probabilistic predictions.","skillfed_tags":["time-series","forecasting","foundation-models"],"use_cases":["Forecast electricity prices or demand using historical data and optional future weather or market covariates.","Generate probabilistic predictions for stock prices or financial metrics with quantile-based confidence intervals.","Predict website traffic, server load, or system metrics for capacity planning without retraining.","Perform rapid prototyping of time series predictions on new datasets without labeled training data.","Deploy production forecasting endpoints on AWS SageMaker or AutoGluon-Cloud for real-time or batch inference."],"what_it_does":"Chronos is a library that wraps pretrained language-model-based time series forecasting models. It transforms historical time series data into token sequences, uses transformer architectures to learn patterns, and generates probabilistic forecasts by sampling multiple future trajectories or computing quantile predictions. The library includes three model families: Chronos-2 (latest, 120M parameters, best zero-shot performance), Chronos-Bolt (patch-based, up to 250 times faster and 20 times more memory-efficient), and the original Chronos (T5-based, ranging from 8M to 710M parameters).\n\nYou use it by loading a pretrained model via HuggingFace, passing historical time series data as a pandas DataFrame with optional covariate columns, and calling predict_df to get quantile forecasts for future steps. The library handles the tokenization, model inference, and output formatting automatically. It's designed for zero-shot forecasting\u2014applying a model trained on diverse time series to new domains without fine-tuning\u2014and supports both univariate (single target column) and multivariate (multiple targets plus exogenous features) scenarios.","worth_installing":"Yes. Chronos is actively maintained, has no known vulnerabilities, and offers a straightforward interface to state-of-the-art pretrained models. Install it if you need time series forecasting without the overhead of training your own model. The low install friction and permissive license make it a practical choice for prototyping and production use. Consider it essential if you're forecasting on new domains where labeled training data is scarce."},"id":"chronos-forecasting","links":{"html":"https://skillfed.io/packages/chronos-forecasting","md":"https://skillfed.io/packages/chronos-forecasting.md","pypi":"https://pypi.org/project/chronos-forecasting/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-02","license_spdx":null,"license_treatment":"permissive","name":"chronos-forecasting","python_support":"supports_current","summary":"Chronos: Pretrained models for time series forecasting"},"popularity":{"monthly_downloads":666996,"position":5423,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.3.1"}