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16 changes: 8 additions & 8 deletions DESCRIPTION
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@@ -1,8 +1,8 @@
Package: smooth
Type: Package
Title: Forecasting Using State Space Models
Version: 3.1.4
Date: 2021-12-01
Version: 3.1.5
Date: 2022-01-26
Authors@R: person("Ivan", "Svetunkov", email = "ivan@svetunkov.ru", role = c("aut", "cre"),
comment="Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK")
URL: https://github.com/config-i1/smooth
Expand All @@ -17,19 +17,19 @@ Description: Functions implementing Single Source of Error state space models fo
and several simulation functions. It also allows dealing with intermittent demand based on the
iETS framework (Svetunkov & Boylan, 2019, <doi: 10.13140/RG.2.2.35897.06242>).
License: GPL (>= 2)
Depends: R (>= 3.0.2), greybox (>= 0.6.7)
Depends: R (>= 3.0.2), greybox (>= 1.0.0)
Imports: Rcpp (>= 0.12.3), stats, graphics, grDevices, pracma, statmod,
MASS, nloptr, utils, zoo
LinkingTo: Rcpp, RcppArmadillo (>= 0.8.100.0.0)
Suggests: Mcomp, numDeriv, testthat, knitr, rmarkdown, doMC,
doParallel, foreach
Suggests: numDeriv, testthat, knitr, rmarkdown, doMC, doParallel,
foreach
VignetteBuilder: knitr
RoxygenNote: 7.1.1
RoxygenNote: 7.1.2
Encoding: UTF-8
NeedsCompilation: yes
Packaged: 2021-12-01 08:14:32 UTC; config
Packaged: 2022-01-26 17:03:18 UTC; config
Author: Ivan Svetunkov [aut, cre] (Lecturer at Centre for Marketing Analytics
and Forecasting, Lancaster University, UK)
Maintainer: Ivan Svetunkov <ivan@svetunkov.ru>
Repository: CRAN
Date/Publication: 2021-12-01 09:10:02 UTC
Date/Publication: 2022-01-26 18:20:02 UTC
92 changes: 46 additions & 46 deletions MD5
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@@ -1,18 +1,18 @@
9cb8eeb3d9fd04f63eef1eb18b4be255 *DESCRIPTION
07a53a5444336347a41f29ac5daca9b8 *DESCRIPTION
26e42ff31e8ad1a97fb78225632538f5 *NAMESPACE
ecc4d72266f21948bba524ba3752a43e *NEWS
e5140370909b3973b4f6f26e26229204 *NEWS
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81f2d289eeb2ed694f29227b9103fe51 *R/isFunctions.R
041d01baa73ad0ec1dad2547505e8678 *R/iss.R
Expand All @@ -29,52 +29,52 @@ a26210beaf498241d089d307cb553e10 *R/simgum.R
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Expand All @@ -95,7 +95,7 @@ c7c700f2093c421a10cfcef28828bae5 *man/sim.sma.Rd
f9abd11f3a036809d9b84a093a3e55dd *man/sim.ssarima.Rd
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b916a4f20c5c02afd8eb54492be95209 *man/ves.Rd
Expand All @@ -113,21 +113,21 @@ f55c9afbd029545484e252c251b475ef *src/adamRefitter.cpp
d31b1c1655ca634aa87e441089f7ea61 *src/ssOccurrence.cpp
6c9cbb26f91aff4495e255bf7b6d6200 *src/ssSimulator.cpp
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9 changes: 9 additions & 0 deletions NEWS
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@@ -1,3 +1,12 @@
smooth v3.1.5 (Release data: 2022-01-26)
=======

Changes:
* summary.adam now prints the standard deviation of the error term (sigma(ourModel)).
* An explanation of the default nsim value in forecast.adam.
* Removed Mcomp from suggests.


smooth v3.1.4 (Release data: 2021-12-01)
=======

Expand Down
50 changes: 28 additions & 22 deletions R/adam.R
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Expand Up @@ -52,10 +52,10 @@ utils::globalVariables(c("adamFitted","algorithm","arEstimate","arOrders","arReq
# \item \link[stats]{dlogis} - Logistic Distribution,
# \item \link[stats]{dt} - T distribution,
# \item \link[greybox]{dalaplace} - Asymmetric Laplace distribution,
#' \item \link[stats]{dlnorm} - Log normal distribution,
# \item dllaplace - Log Laplace distribution,
# \item dls - Log S distribution,
# \item dlgnorm - Log Generalised Normal distribution,
#' \item \link[stats]{dlnorm} - Log-Normal distribution,
# \item dllaplace - Log-Laplace distribution,
# \item dls - Log-S distribution,
# \item dlgnorm - Log-Generalised Normal distribution,
# \item \link[greybox]{dbcnorm} - Box-Cox normal distribution,
#' \item \link[stats]{dgamma} - Gamma distribution,
#' \item \link[statmod]{dinvgauss} - Inverse Gaussian distribution,
Expand Down Expand Up @@ -364,11 +364,11 @@ utils::globalVariables(c("adamFitted","algorithm","arEstimate","arOrders","arReq
#' @export adam
adam <- function(data, model="ZXZ", lags=c(frequency(data)), orders=list(ar=c(0),i=c(0),ma=c(0),select=FALSE),
constant=FALSE, formula=NULL, regressors=c("use","select","adapt"),
outliers=c("ignore","use","select"), level=0.99,
occurrence=c("none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"),
distribution=c("default","dnorm","dlaplace","ds","dgnorm",
"dlnorm","dinvgauss","dgamma"),
loss=c("likelihood","MSE","MAE","HAM","LASSO","RIDGE","MSEh","TMSE","GTMSE","MSCE"),
outliers=c("ignore","use","select"), level=0.99,
h=0, holdout=FALSE,
persistence=NULL, phi=NULL, initial=c("optimal","backcasting"), arma=NULL,
ic=c("AICc","AIC","BIC","BICc"), bounds=c("usual","admissible","none"),
Expand Down Expand Up @@ -5237,7 +5237,7 @@ plot.adam <- function(x, which=c(1,2,4,6), level=0.95, legend=FALSE,
}
else if(any(x$distribution=="dlnorm")){
if(!any(names(ellipsis)=="main")){
ellipsis$main <- "QQ plot of Log Normal distribution";
ellipsis$main <- "QQ plot of Log-Normal distribution";
}
ellipsis$x <- qlnorm(ppoints(500), meanlog=0, sdlog=x$scale);

Expand All @@ -5255,7 +5255,7 @@ plot.adam <- function(x, which=c(1,2,4,6), level=0.95, legend=FALSE,
}
else if(x$distribution=="dllaplace"){
if(!any(names(ellipsis)=="main")){
ellipsis$main <- "QQ-plot of Log Laplace distribution";
ellipsis$main <- "QQ-plot of Log-Laplace distribution";
}
ellipsis$x <- exp(qlaplace(ppoints(500), mu=0, scale=x$scale));

Expand All @@ -5273,7 +5273,7 @@ plot.adam <- function(x, which=c(1,2,4,6), level=0.95, legend=FALSE,
}
else if(x$distribution=="dls"){
if(!any(names(ellipsis)=="main")){
ellipsis$main <- "QQ-plot of Log S distribution";
ellipsis$main <- "QQ-plot of Log-S distribution";
}
ellipsis$x <- exp(qs(ppoints(500), mu=0, scale=x$scale));

Expand All @@ -5291,7 +5291,7 @@ plot.adam <- function(x, which=c(1,2,4,6), level=0.95, legend=FALSE,
}
else if(x$distribution=="dlgnorm"){
if(!any(names(ellipsis)=="main")){
ellipsis$main <- paste0("QQ-plot of Log Generalised Normal distribution with shape=",round(x$other$shape,3));
ellipsis$main <- paste0("QQ-plot of Log-Generalised Normal distribution with shape=",round(x$other$shape,3));
}
ellipsis$x <- exp(qgnorm(ppoints(500), mu=0, scale=x$scale, shape=x$other$shape));

Expand Down Expand Up @@ -5734,10 +5734,10 @@ print.adam <- function(x, digits=4, ...){
"dlogis" = "Logistic",
"dt" = paste0("Student t with nu=",round(x$other$nu, digits)),
"dalaplace" = paste0("Asymmetric Laplace with alpha=",round(x$other$alpha,digits)),
"dlnorm" = "Log Normal",
"dllaplace" = "Log Laplace",
"dls" = "Log S",
"dlgnorm" = paste0("Log Generalised Normal with shape=",round(x$other$shape, digits)),
"dlnorm" = "Log-Normal",
"dllaplace" = "Log-Laplace",
"dls" = "Log-S",
"dlgnorm" = paste0("Log-Generalised Normal with shape=",round(x$other$shape, digits)),
# "dbcnorm" = paste0("Box-Cox Normal with lambda=",round(x$other$lambda,2)),
"dinvgauss" = "Inverse Gaussian",
"dgamma" = "Gamma"
Expand Down Expand Up @@ -6253,6 +6253,7 @@ summary.adam <- function(object, level=0.95, bootstrap=FALSE, ...){
ourReturn$nParam <- object$nParam;
ourReturn$call <- object$call;
ourReturn$other <- object$other;
ourReturn$sigma <- sigma(object);

if(object$loss=="likelihood" ||
(any(object$loss==c("MSE","MSEh","MSCE")) & (object$distribution=="dnorm")) ||
Expand Down Expand Up @@ -6302,10 +6303,10 @@ print.summary.adam <- function(x, ...){
"dlogis" = "Logistic",
"dt" = paste0("Student t with nu=",round(x$other$nu, digits)),
"dalaplace" = paste0("Asymmetric Laplace with alpha=",round(x$other$alpha,digits)),
"dlnorm" = "Log Normal",
"dllaplace" = "Log Laplace",
"dls" = "Log S",
"dlgnorm" = paste0("Log Generalised Normal with shape=",round(x$other$shape,digits)),
"dlnorm" = "Log-Normal",
"dllaplace" = "Log-Laplace",
"dls" = "Log-S",
"dlgnorm" = paste0("Log-Generalised Normal with shape=",round(x$other$shape,digits)),
# "dbcnorm" = paste0("Box-Cox Normal with lambda=",round(x$other$lambda,2)),
"dinvgauss" = "Inverse Gaussian",
"dgamma" = "Gamma"
Expand Down Expand Up @@ -6339,6 +6340,7 @@ print.summary.adam <- function(x, ...){
cat("\nAll coefficients were provided");
}

cat("\nError standard deviation:", round(x$sigma,digits));
cat("\nSample size:", x$nobs);
cat("\nNumber of estimated parameters:", x$nparam);
cat("\nNumber of degrees of freedom:", x$nobs-x$nparam);
Expand Down Expand Up @@ -7248,7 +7250,11 @@ plot.adam.predict <- function(x, ...){

# Work in progress...
#' @param newdata The new data needed in order to produce forecasts.
#' @param nsim Number of iterations to do in case of \code{interval="simulated"}.
#' @param nsim Number of iterations to do in cases of \code{interval="simulated"},
#' \code{interval="prediction"} (for mixed and multiplicative model),
#' \code{interval="confidence"} and \code{interval="complete"}.
#' The default value for the prediction / simulated interval is 1000. In case of
#' confidence or complete intervals, this is set to 100.
#' @param interval What type of mechanism to use for interval construction.
#' For ADAM: the
#' recommended option is \code{interval="prediction"}, which will use analytical
Expand Down Expand Up @@ -8218,10 +8224,10 @@ plot.adam.forecast <- function(x, ...){
"dgnorm" = paste0("Generalised Normal with shape=",round(x$model$other$shape,digits)),
"dalaplace" = paste0("Asymmetric Laplace with alpha=",round(x$model$other$alpha,digits)),
"dt" = paste0("Student t with nu=",round(x$model$other$nu, digits)),
"dlnorm" = "Log Normal",
"dllaplace" = "Log Laplace",
"dls" = "Log S",
"dgnorm" = paste0("Log Generalised Normal with shape=",round(x$model$other$shape,digits)),
"dlnorm" = "Log-Normal",
"dllaplace" = "Log-Laplace",
"dls" = "Log-S",
"dgnorm" = paste0("Log-Generalised Normal with shape=",round(x$model$other$shape,digits)),
# "dbcnorm" = paste0("Box-Cox Normal with lambda=",round(x$other$lambda,2)),
"dinvgauss" = "Inverse Gaussian",
"dgamma" = "Gamma",
Expand Down
10 changes: 6 additions & 4 deletions R/autoadam.R
View file
Expand Up @@ -15,14 +15,16 @@
#' @rdname adam
#' @importFrom stats update.formula
#' @export
auto.adam <- function(data, model="ZXZ", lags=c(frequency(data)), orders=list(ar=c(0),i=c(0),ma=c(0),select=FALSE),
formula=NULL, outliers=c("ignore","use","select"), level=0.99,
auto.adam <- function(data, model="ZXZ", lags=c(frequency(data)),
orders=list(ar=c(0),i=c(0),ma=c(0),select=FALSE),
formula=NULL, regressors=c("use","select","adapt"),
occurrence=c("none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"),
distribution=c("dnorm","dlaplace","ds","dgnorm","dlnorm","dinvgauss","dgamma"),
outliers=c("ignore","use","select"), level=0.99,
h=0, holdout=FALSE,
persistence=NULL, phi=NULL, initial=c("optimal","backcasting"), arma=NULL,
occurrence=c("none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"),
ic=c("AICc","AIC","BIC","BICc"), bounds=c("usual","admissible","none"),
regressors=c("use","select","adapt"), silent=TRUE, parallel=FALSE, ...){
silent=TRUE, parallel=FALSE, ...){
# Copyright (C) 2020 - Inf Ivan Svetunkov

# Start measuring the time of calculations
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