{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/2"}],"enrichment":{"capability":"Provides fast C++ implementations of time-series transformations and scalers for grouped data, designed to work with numpy arrays and support multithreading.","skillfed_tags":["time-series","forecasting","performance"],"use_cases":["Preprocessing large collections of time series for machine-learning forecasting pipelines where scaling and lag features are needed.","Computing expanding-window statistics (mean, standard deviation) across many independent time series efficiently.","Applying rolling-window operations like moving averages to grouped time-series data with minimal Python overhead.","Standardizing or normalizing multiple time series in batch using LocalStandardScaler or similar transformers.","Serving as the computational backbone for higher-level forecasting libraries that need fast grouped transformations."],"what_it_does":"Coreforecast is a C++ acceleration library for time-series data transformations, built by Nixtla to replace slower Python and numba-based implementations. It operates on grouped arrays\u2014a compact representation where multiple independent time series are stored in a single flattened numpy array with index pointers marking series boundaries\u2014and applies operations like scaling, lag transforms, rolling statistics, and seasonal decomposition with multithreading support.\n\nThe library is designed as a backend for higher-level forecasting tools rather than for direct use in most cases. It depends only on numpy and avoids the cold-start overhead and LLVM dependency of numba-based approaches. You interact with it through a grouped-array abstraction and call transformations like LocalStandardScaler or ExpandingMean; the library handles the C++ computation and parallelization internally.","worth_installing":"Yes, if you are working with time-series forecasting pipelines and need fast grouped transformations. The library is actively maintained, has no known vulnerabilities, and carries a permissive Apache-2.0 license. However, most users should access it indirectly through mlforecast or similar higher-level libraries rather than using it directly; install it directly only if you need fine-grained control over grouped-array operations or are building a forecasting tool."},"id":"coreforecast","links":{"html":"https://skillfed.io/packages/coreforecast","md":"https://skillfed.io/packages/coreforecast.md","pypi":"https://pypi.org/project/coreforecast/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-09","license_spdx":null,"license_treatment":"permissive","name":"coreforecast","python_support":"supports_current","summary":"Fast implementations of common forecasting routines"},"popularity":{"monthly_downloads":1900154,"position":3444,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.0.18"}