Hierarchical Time-Series Forecasting

Source: R/Lrnr_hts.R

This learner supports prediction using hierarchical time-series modeling, using hts. Fitting is done with hts and prediction is performed via forecast.gts.

Format

R6Class object.

Value

Learner object with methods for training and prediction. See Lrnr_base for documentation on learners.

Parameters

method)

Method for distributing forecasts within hierarchy. See details of forecast.gts.

weights)

Weights used for "optimal combination" method: weights="ols" uses an unweighted combination (as described in Hyndman et al 2011); weights="wls" uses weights based on forecast variances (as described in Hyndman et al 2015); weights="mint" uses a full covariance estimate to determine the weights (as described in Hyndman et al 2016); weights="nseries" uses weights based on the number of series aggregated at each node.

fmethod)

Forecasting method to use for each series.

algorithms)

An algorithm to be used for computing the combination forecasts (when method=="comb"). The combination forecasts are based on an ill-conditioned regression model. "lu" indicates LU decomposition is used; "cg" indicates a conjugate gradient method; "chol" corresponds to a Cholesky decomposition; "recursive" indicates the recursive hierarchical algorithm of Hyndman et al (2015); "slm" uses sparse linear regression. Note that algorithms = "recursive" and algorithms = "slm" cannot be used if weights="mint".

covariance)

Type of the covariance matrix to be used with weights="mint": either a shrinkage estimator ("shr") with shrinkage towards the diagonal; or a sample covariance matrix ("sam").

keep.fitted)

If TRUE, keep fitted values at the bottom level.

keep.resid)

If TRUE, keep residuals at the bottom level.

positive)

If TRUE, forecasts are forced to be strictly positive (by setting lambda=0).

lambda)

Box-Cox transformation parameter.

level

Level used for "middle-out" method (only used when method = "mo").

parallel

If TRUE, import parallel to allow parallel processing.

num.cores

If parallel = TRUE, specify how many cores are going to be used.