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JoaquinAmatRodrigo avatar

JoaquinAmatRodrigo/skforecast

0
View on GitHub↗
1,503 stars·193 forks·Python·BSD-3-Clause·3 viewsskforecast.org↗

Skforecast

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models

Features

  • Time Series - Forecasting tools using machine learning models.

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Open-source alternatives to Skforecast

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    sktime is a machine learning framework designed for time series analysis. It provides a unified interface for performing time series forecasting, classification, and anomaly detection, integrating these capabilities into a standardized toolkit compatible with the scikit-learn API. The framework allows for the construction of complex analysis workflows through model pipelining and ensemble-based aggregation. It uses adapter-based integration to wrap external time series libraries, providing a single entry point for diverse algorithmic implementations. Its capabilities cover temporal data tran

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Frequently asked questions

What does joaquinamatrodrigo/skforecast do?

Python library for time series forecasting using scikit-learn compatible models, statistical methods, and foundation models

What are the main features of joaquinamatrodrigo/skforecast?

The main features of joaquinamatrodrigo/skforecast are: Time Series.

What are some open-source alternatives to joaquinamatrodrigo/skforecast?

Open-source alternatives to joaquinamatrodrigo/skforecast include: alan-turing-institute/sktime — sktime is a machine learning framework designed for time series analysis. It provides a unified interface for… christophmark/bayesloop — Probabilistic programming framework that facilitates objective model selection for time-varying parameter models. facebook/prophet — Prophet is a time series forecasting library and decomposition tool that uses an additive regression model to predict… linkedin/greykite — A flexible, intuitive and fast forecasting library. linkedin/luminol — Anomaly Detection and Correlation library. nixtla/mlforecast — Scalable machine 🤖 learning for time series forecasting.