{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Python SDK for accessing TimeGPT, a foundation model for time series forecasting and anomaly detection via API.","skillfed_tags":["time-series","forecasting","api-client"],"use_cases":["Forecast electricity demand or retail sales for the next 24 hours with confidence intervals.","Detect anomalies in IoT sensor streams or financial time series without labeled training data.","Fine-tune TimeGPT on domain-specific historical data to improve accuracy for your particular use case.","Integrate forecasting into a web application via the Python SDK without building a custom model.","Run forecasting directly in Snowflake using stored procedures without exporting data."],"what_it_does":"Nixtla is a Python client for TimeGPT, a transformer-based foundation model trained specifically for time series analysis. It provides a low-code interface to forecast future values and detect anomalies in time series data across domains like retail, electricity, finance, and IoT. The package wraps an external API, so you instantiate a client with an API key, pass your data as a pandas DataFrame, and call methods like `forecast()` or `detect_anomalies()` to get results.\n\nThe model works zero-shot\u2014it requires no prior training on your specific data\u2014and supports fine-tuning, exogenous variables, prediction intervals, cross-validation, and irregular timestamps. It depends on pandas for data handling, pydantic for configuration, httpx for HTTP calls, tenacity for retry logic, and several utility packages. The package also supports Snowflake deployment via optional extras.","worth_installing":"Yes, if you need time series forecasting or anomaly detection and have access to an API key. The low install friction, active maintenance, permissive license, and zero-shot capability make it a practical choice for rapid prototyping. The external API dependency and lack of offline capability are trade-offs; verify cost and rate limits before production use."},"id":"nixtla","links":{"html":"https://skillfed.io/packages/nixtla","md":"https://skillfed.io/packages/nixtla.md","pypi":"https://pypi.org/project/nixtla/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-21","license_spdx":null,"license_treatment":"permissive","name":"nixtla","python_support":"supports_current","summary":"Python SDK for Nixtla API (TimeGPT)"},"popularity":{"monthly_downloads":125589,"position":11808,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.0"}