{"categories":[{"label":"Software Development","url":"https://skillfed.io/packages/category/software-development/3"},{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"Pmdarima provides automated ARIMA time series forecasting with a scikit-learn-compatible interface, wrapping statsmodels to deliver R's auto.arima functionality in Python.","skillfed_tags":["time-series-forecasting","arima","scikit-learn-compatible"],"use_cases":["Automatically select optimal ARIMA parameters and fit a forecasting model without manual hyperparameter tuning.","Build a preprocessing pipeline combining Box-Cox transformation with seasonal ARIMA for improved forecast accuracy.","Serialize and deploy a fitted time series model to production using pickle, then load and predict on new data.","Detect and handle seasonality in sales, demand, or financial data using seasonal decomposition and m-parameter tuning.","Perform cross-validation on time series splits to evaluate model generalization before deployment."],"what_it_does":"Pmdarima is a statistical library that brings R's auto.arima forecasting capability to Python with a scikit-learn-style API. It wraps statsmodels internally but presents a more accessible interface for developers familiar with scikit-learn's estimator and pipeline patterns. The package includes automated ARIMA order selection, stationarity and seasonality tests, time series transformations (Box-Cox, Fourier), seasonal decomposition, cross-validation utilities, and built-in datasets for prototyping.\n\nYou use it to fit forecasting models on historical time series data and generate predictions for future periods. It supports both simple auto-ARIMA calls and complex preprocessing pipelines with multiple transformers. The library targets financial, insurance, and research domains where time series forecasting is central. Its main dependencies\u2014numpy, pandas, scikit-learn, scipy, statsmodels\u2014are standard in the data science ecosystem, making it a natural fit for projects already using those tools.","worth_installing":"Yes, if you need automated ARIMA forecasting with scikit-learn integration. The package is mature, well-maintained, MIT-licensed, and widely downloaded (top 5000 PyPI). Install friction is moderate due to multiple dependencies and potential C compilation, but prebuilt wheels for Python 3.10+ on major platforms mitigate this. No known security vulnerabilities. Suitable for production time series work in finance, insurance, and research."},"id":"pmdarima","links":{"html":"https://skillfed.io/packages/pmdarima","md":"https://skillfed.io/packages/pmdarima.md","pypi":"https://pypi.org/project/pmdarima/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-11-17","license_spdx":"MIT","license_treatment":"permissive","name":"pmdarima","python_support":"supports_current","summary":"Python's forecast::auto.arima equivalent"},"popularity":{"monthly_downloads":3290471,"position":2672,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.1.1"}