Title: A Basis for Modular Model Creation
person("Joshua", "Kaminsky", email = "jkaminsky@jhu.edu", role = c("aut", "cre")),
person("Justin", "Lessler", email = "justin@jhu.edu", role = c("aut")),
person("Nicholas", "Reich", email = "nick@schoolph.umass.edu", role = c("aut")))
Description: Create modular models.  Quickly prototype models whose input includes (multiple) time series data.  Create pieces of model use cases separately, and swap out particular models as desired. Create modeling competitions, data processing pipelines, and re-useable models.
Packaged: 2019-06-11 16:34:13 UTC; jkaminsky

Packaged: 2019-11-03 01:59:52 UTC; jkaminsky

Packaged: 2020-01-16 14:05:20 UTC; jkaminsky

Authors@R: person("Joshua", "Kaminsky", email = "jkaminsky@jhu.edu", role = c("aut", "cre"))
Packaged: 2017-06-27 22:01:22 UTC; jkaminsky

Title: Test Bench for the Comparison of Forecast Methods
Maintainer: Neeraj Dhanraj Bokde <neerajdhanraj@gmail.com>
Description: Provides a test bench for the comparison of forecasting
methods in uni-variate time series. Forecasting methods are compared using
different error metrics. Proposed forecasting methods and alternative error
metrics can be used. Detailed discussion is provided in the vignette.
Imports: PSF, decomposedPSF, ggplot2, gridExtra, imputeTestbench,
methods, reshape2, forecast, circlize, RColorBrewer, stats,

Description: Supervised, multivariate, and non-parametric discretization algorithm based on tree ensembles learning and moment matching optimization. This version of the algorithm relies on random forest algorithm to learn a large set of split points that conserves the relationship between attributes and the target class, and on moment matching optimization to transform this set into a reduced number of cut points matching as well as possible statistical properties of the initial set of split points. For each attribute to be discretized, the set S of its related split points extracted through random forest is mapped to a reduced set C of cut points of size k. This mapping relies on minimizing, for each continuous attribute to be discretized, the distance between the four first moments of S and the four first moments of C subject to some constraints. This non-linear optimization problem is performed using k values ranging from 2 to 'max_splits', and the best solution returned correspond to the value k which optimum solution is the lowest one over the different realizations. ForestDisc is a generalization of RFDisc discretization method initially proposed by Berrado and Runger (2009) <doi:10.1109/AICCSA.2009.5069327>, and improved by Berrado et al. in 2012 by adopting the idea of moment matching optimization related by Hoyland and Wallace (2001) <doi: 10.1287/mnsc.47.2.295.9834>.
Imports: randomForest, nloptr, moments, stats