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cff-version: 1.2.0
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type: software
license: MIT
title: 'GACE: Generalized Adaptive Capped Estimator for Time Series Forecasting'
version: 1.0.0
doi: 10.32614/CRAN.package.GACE
abstract: Provides deterministic forecasting for weekly, monthly, quarterly, and yearly
  time series using the Generalized Adaptive Capped Estimator. The method includes
  preprocessing steps for handling missing and extreme values, extraction of multiple
  growth components (including long-term, short-term, rolling, and drift-based signals),
  volatility-aware asymmetric capping, optional seasonal adjustment through damped
  and normalized seasonal factors, and a recursive forecast formulation with moderated
  growth. The main interface 'gace_forecast()' returns forecasts in a consistent structure,
  and 'plot_gace()' offers visualization support. Related forecasting background is
  discussed in Hyndman and Athanasopoulos (2021) <https://otexts.com/fpp3/> and Hyndman
  and Khandakar (2008) <https://doi.org/10.18637/jss.v027.i03>.The method extends
  classical extrapolative forecasting approaches and is suited for operational and
  business planning contexts where stability and interpretability are important.
authors:
- family-names: Gunasekaran
  given-names: Vinodhkumar
  email: vinoalles@gmail.com
repository: https://vinoalles.r-universe.dev
repository-code: https://github.com/vinoalles/GACE
commit: 5507d850e7e5d02daec772f2297365cb094e5f93
url: https://github.com/vinoalles/GACE
date-released: '2025-11-27'
contact:
- family-names: Gunasekaran
  given-names: Vinodhkumar
  email: vinoalles@gmail.com