R-universe search: exports:comb

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ceweiss

ForecastComb:Forecast Combination Methods

Provides geometric- and regression-based forecast combination methods under a unified user interface for the packages 'ForecastCombinations' and 'GeomComb'. Additionally, updated tools and convenience functions for data pre-processing are available in order to deal with common problems in forecast combination (missingness, collinearity). For method details see Hsiao C, Wan SK (2014). <doi:10.1016/j.jeconom.2013.11.003>, Hansen BE (2007). <doi:10.1111/j.1468-0262.2007.00785.x>, Elliott G, Gargano A, Timmermann A (2013). <doi:10.1016/j.jeconom.2013.04.017>, and Clemen RT (1989). <doi:10.1016/0169-2070(89)90012-5>.

Maintained by Christoph E. Weiss. Last updated 7 years ago.

30 stars 4.77 score 39 scripts

techtonique

ahead:Time Series Forecasting with uncertainty quantification

Univariate and multivariate time series forecasting with uncertainty quantification.

Maintained by T. Moudiki. Last updated 1 months ago.

forecasting machine-learning predictive-modeling statistical-learning time-series time-series-forecasting uncertainty-quantification cpp

21 stars 4.63 score 51 scripts

cran

GeomComb:(Geometric) Forecast Combination Methods

Provides eigenvector-based (geometric) forecast combination methods; also includes simple approaches (simple average, median, trimmed and winsorized mean, inverse rank method) and regression-based combination. Tools for data pre-processing are available in order to deal with common problems in forecast combination (missingness, collinearity).

Maintained by Christoph E. Weiss. Last updated 8 years ago.

1.70 score