{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/9"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"}],"enrichment":{"capability":"Darts provides forecasting and anomaly detection for time series using a unified scikit-learn-style API, supporting classical models like ARIMA, deep neural networks, multivariate series, and probabilistic predictions.","skillfed_tags":["time-series-forecasting","anomaly-detection","neural-networks"],"use_cases":["Forecast demand, sales, or resource usage over time using ARIMA, exponential smoothing, or neural networks.","Detect anomalies in sensor data, system metrics, or financial time series using k-means or other scorers.","Train a single model on multiple related time series (e.g., sales across stores) to improve generalization.","Generate probabilistic forecasts with quantile intervals for risk-aware decision-making.","Combine predictions from multiple models to reduce forecast error through ensemble methods.","Incorporate external variables (weather, promotions) as past or future covariates to improve predictions."],"what_it_does":"Darts is a time series forecasting and anomaly detection library that unifies classical statistical models (ARIMA, exponential smoothing) and modern deep learning approaches under a single scikit-learn-compatible interface. It handles univariate and multivariate series, supports probabilistic forecasting with confidence intervals, and enables training on multiple series simultaneously.\n\nThe library's anomaly detection module wraps forecasting models and integrates PyOD scorers to detect outliers in time series. It also supports external covariates (past-observed and future-known), static metadata, hierarchical reconciliation, and conformal prediction for calibrated uncertainty quantification. The core TimeSeries object abstracts data handling, making it straightforward to preprocess, split, and backtest models.","worth_installing":"Yes. Darts is actively maintained, widely used (top 15000 on PyPI), has no known vulnerabilities, and offers a comprehensive, production-ready toolkit for time series forecasting and anomaly detection. The scikit-learn-style API and support for both classical and deep learning models make it suitable for both prototyping and deployment. The 18 dependencies are standard ML libraries, not exotic or risky."},"id":"darts","links":{"html":"https://skillfed.io/packages/darts","md":"https://skillfed.io/packages/darts.md","pypi":"https://pypi.org/project/darts/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-20","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"darts","python_support":"supports_current","summary":"A python library for easy manipulation and forecasting of time series."},"popularity":{"monthly_downloads":299040,"position":7864,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.46.1"}