Arima Python Packages

pmdarima

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

statsforecast

Lightning ⚡️ fast forecasting with statistical and econometric models.

skforecast

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

orbit-ml

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

edgar-analytics

EDGAR Analytics – Python Library for Extracting, Analyzing, and Forecasting SEC EDGAR Filings. Streamline your financial analysis with comprehensive metrics, growth rates, and automated reporting capabilities.

orange3-timeseries

🍊 :chart_with_upwards_trend: Orange add-on for analyzing, visualizing, manipulating, and forecasting time series data.

vectrix

Zero-config time series forecasting for Python. 30+ models, auto selection, Forecast DNA, Rust turbo — one line of code.

auto-ts

Automatically build ARIMA, SARIMAX, VAR, FB Prophet and XGBoost Models on Time Series data sets with a Single Line of Code. Created by Ram Seshadri. Collaborators welcome.

pyramid-arima

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

bluewhale3-timeseries

用于探索时间序列和顺序数据的蓝鲸插件。

ts-app

A simple dashboard application for interactively fitting ARIMA models.

runhic

An easy-to-use Hi-C data processing software supporting distributed computation.

tsa-course

A collection of scripts and functions used in the course 'Time Series Analysis with Python'

timesage-ts

The Wise Time Series Library — Beautiful EDA, All Models, Plain-English Interpretation

blauwal3-timeseries

🍊 :chart_with_upwards_trend: Orange add-on for analyzing, visualizing, manipulating, and forecasting time series data.

orange3-timeseries-zh

🍊 :chart_with_upwards_trend: Orange add-on for analyzing, visualizing, manipulating, and forecasting time series data.