version 2.5.4

cran · Oct 22, 2019 · 390e01c · 390e01c
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: smooth
 Type: Package
 Title: Forecasting Using State Space Models
-Version: 2.5.3
-Date: 2019-08-19
+Version: 2.5.4
+Date: 2019-10-22
 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
@@ -26,9 +26,9 @@ VignetteBuilder: knitr
 RoxygenNote: 6.1.1
 Encoding: UTF-8
 NeedsCompilation: yes
-Packaged: 2019-08-19 13:10:03 UTC; config
+Packaged: 2019-10-21 23:05:59 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: 2019-08-19 18:10:06 UTC
+Date/Publication: 2019-10-22 16:50:02 UTC
diff --git a/MD5 b/MD5
@@ -1,86 +1,86 @@
-36f515f24de7c9c579d91f7f14a906d1 *DESCRIPTION
+15cd170a6b1c4902b7839640ad2be383 *DESCRIPTION
 1799da829fb32a408fc4351ad888c2f0 *NAMESPACE
-3f57bafb34ace7cda02d9ca1c332166f *NEWS
+6a2f64a24c2dc2d8711caa9b9d3656b2 *NEWS
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 86598f88c0a95d24863e270455fbf77f *man/forecast.smooth.Rd
-5534d246ca6e76b7e4899895579591c9 *man/gum.Rd
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 7cb683020af82bfef06e6ebe35bb4004 *man/isFunctions.Rd
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@@ -89,18 +89,18 @@ d9be59d43ba1e8339e9052a7b8d2f927 *man/sim.gum.Rd
 1ee9bd971c6944ffe5bb7b1b78457fde *man/sim.sma.Rd
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 0bed3336877e918ead66d79ba752cfe1 *man/viss.Rd
 9859afefaf6500832d7469cadbbb28c8 *src/Makevars
 a6850c2998c396b505104b800670480e *src/Makevars.win
 d736b0ac3c57296a62ff17c520142d0c *src/RcppExports.cpp
 9fb0915a4e7ffe192150808e5e890c69 *src/registerDynamicSymbol.c
-65b2f8a2577e157d75f4104623cce7a2 *src/ssGeneral.cpp
+d8c29fb21f816537b9c793ce9403db95 *src/ssGeneral.cpp
 1ff530213052894349cca6864ef67a38 *src/ssGeneral.h
 d31b1c1655ca634aa87e441089f7ea61 *src/ssOccurrence.cpp
 cccddc1650403630c1f85f6bc9a2ac63 *src/ssSimulator.cpp

diff --git a/NEWS b/NEWS
@@ -1,3 +1,13 @@
+smooth v2.5.4 (Release data: 2019-10-22)
+==============
+
+Changes:
+* loss="TFL" is now called loss="GPL" - General Predictive Likelihood.
+* The return of unbiased estimate of variance (division by T-k)... We now use it for the calculation of the all the prediction interval types, but there is also an option of using the biased (division by T) parametric prediction interval with interval="likelihood".
+* Fix for the initials in the optimiser in case of difficult mixed mdels.
+* Updated the description of the bounds parameter in ves().
+
+
 smooth v2.5.3 (Release data: 2019-08-19)
 ==============
 

diff --git a/R/autoces.R b/R/autoces.R
@@ -18,6 +18,7 @@ utils::globalVariables(c("silentText","silentGraph","silentLegend","initialType"
 #'
 #' @template ssBasicParam
 #' @template ssAdvancedParam
+#' @template ssIntervals
 #' @template ssInitialParam
 #' @template ssAuthor
 #' @template ssKeywords
@@ -58,7 +59,7 @@ auto.ces <- function(y, models=c("none","simple","full"),
                 initial=c("backcasting","optimal"), ic=c("AICc","AIC","BIC","BICc"),
                 loss=c("MSE","MAE","HAM","MSEh","TMSE","GTMSE","MSCE"),
                 h=10, holdout=FALSE, cumulative=FALSE,
-                interval=c("none","parametric","semiparametric","nonparametric"), level=0.95,
+                interval=c("none","parametric","likelihood","semiparametric","nonparametric"), level=0.95,
                 occurrence=c("none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"),
                 oesmodel="MNN",
                 bounds=c("admissible","none"),

diff --git a/R/autogum.R b/R/autogum.R
@@ -18,6 +18,7 @@ utils::globalVariables(c("silentText","silentGraph","silentLegend","initialType"
 #'
 #' @template ssBasicParam
 #' @template ssAdvancedParam
+#' @template ssIntervals
 #' @template ssInitialParam
 #' @template ssAuthor
 #' @template ssKeywords
@@ -59,7 +60,7 @@ auto.gum <- function(y, orders=3, lags=frequency(y), type=c("additive","multipli
                      initial=c("backcasting","optimal"), ic=c("AICc","AIC","BIC","BICc"),
                      loss=c("MSE","MAE","HAM","MSEh","TMSE","GTMSE","MSCE"),
                      h=10, holdout=FALSE, cumulative=FALSE,
-                     interval=c("none","parametric","semiparametric","nonparametric"), level=0.95,
+                     interval=c("none","parametric","likelihood","semiparametric","nonparametric"), level=0.95,
                      occurrence=c("none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"),
                      oesmodel="MNN",
                      bounds=c("restricted","admissible","none"),

diff --git a/R/automsarima.R b/R/automsarima.R
@@ -22,6 +22,7 @@ utils::globalVariables(c("silentText","silentGraph","silentLegend","initialType"
 #'
 #' @template ssBasicParam
 #' @template ssAdvancedParam
+#' @template ssIntervals
 #' @template ssInitialParam
 #' @template ssAuthor
 #' @template ssKeywords
@@ -84,7 +85,7 @@ auto.msarima <- function(y, orders=list(ar=c(3,3),i=c(2,1),ma=c(3,3)), lags=c(1,
                          initial=c("backcasting","optimal"), ic=c("AICc","AIC","BIC","BICc"),
                          loss=c("MSE","MAE","HAM","MSEh","TMSE","GTMSE","MSCE"),
                          h=10, holdout=FALSE, cumulative=FALSE,
-                         interval=c("none","parametric","semiparametric","nonparametric"), level=0.95,
+                         interval=c("none","parametric","likelihood","semiparametric","nonparametric"), level=0.95,
                          occurrence=c("none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"),
                          oesmodel="MNN",
                          bounds=c("admissible","none"),

diff --git a/R/autossarima.R b/R/autossarima.R
@@ -22,6 +22,7 @@ utils::globalVariables(c("silentText","silentGraph","silentLegend","initialType"
 #'
 #' @template ssBasicParam
 #' @template ssAdvancedParam
+#' @template ssIntervals
 #' @template ssInitialParam
 #' @template ssAuthor
 #' @template ssKeywords
@@ -84,7 +85,7 @@ auto.ssarima <- function(y, orders=list(ar=c(3,3),i=c(2,1),ma=c(3,3)), lags=c(1,
                          initial=c("backcasting","optimal"), ic=c("AICc","AIC","BIC","BICc"),
                          loss=c("MSE","MAE","HAM","MSEh","TMSE","GTMSE","MSCE"),
                          h=10, holdout=FALSE, cumulative=FALSE,
-                         interval=c("none","parametric","semiparametric","nonparametric"), level=0.95,
+                         interval=c("none","parametric","likelihood","semiparametric","nonparametric"), level=0.95,
                          occurrence=c("none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"),
                          oesmodel="MNN",
                          bounds=c("admissible","none"),

diff --git a/R/ces.R b/R/ces.R
@@ -15,6 +15,7 @@ utils::globalVariables(c("silentText","silentGraph","silentLegend","initialType"
 #'
 #' @template ssBasicParam
 #' @template ssAdvancedParam
+#' @template ssIntervals
 #' @template ssInitialParam
 #' @template ssAuthor
 #' @template ssKeywords
@@ -148,7 +149,7 @@ ces <- function(y, seasonality=c("none","simple","partial","full"),
                 initial=c("backcasting","optimal"), A=NULL, B=NULL, ic=c("AICc","AIC","BIC","BICc"),
                 loss=c("MSE","MAE","HAM","MSEh","TMSE","GTMSE","MSCE"),
                 h=10, holdout=FALSE, cumulative=FALSE,
-                interval=c("none","parametric","semiparametric","nonparametric"), level=0.95,
+                interval=c("none","parametric","likelihood","semiparametric","nonparametric"), level=0.95,
                 occurrence=c("none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"),
                 oesmodel="MNN",
                 bounds=c("admissible","none"),

diff --git a/R/es.R b/R/es.R
@@ -43,6 +43,7 @@ utils::globalVariables(c("vecg","nComponents","lagsModel","phiEstimate","yInSamp
 #'
 #' @template ssBasicParam
 #' @template ssAdvancedParam
+#' @template ssIntervals
 #' @template ssPersistenceParam
 #' @template ssAuthor
 #' @template ssKeywords
@@ -255,7 +256,7 @@ es <- function(y, model="ZZZ", persistence=NULL, phi=NULL,
                initial=c("optimal","backcasting"), initialSeason=NULL, ic=c("AICc","AIC","BIC","BICc"),
                loss=c("MSE","MAE","HAM","MSEh","TMSE","GTMSE","MSCE"),
                h=10, holdout=FALSE, cumulative=FALSE,
-               interval=c("none","parametric","semiparametric","nonparametric"), level=0.95,
+               interval=c("none","parametric","likelihood","semiparametric","nonparametric"), level=0.95,
                occurrence=c("none","auto","fixed","general","odds-ratio","inverse-odds-ratio","direct"),
                oesmodel="MNN",
                bounds=c("usual","admissible","none"),
@@ -724,6 +725,8 @@ EstimatorES <- function(...){
     }
 
     # Parameters are chosen to speed up the optimisation process and have decent accuracy
+    # res <- optimx::hjn(C, CF, CLower, CUpper);
+    # C[] <- res$par;
     res <- nloptr(C, CF, lb=CLower, ub=CUpper,
                   opts=list("algorithm"="NLOPT_LN_BOBYQA", "xtol_rel"=xtol_rel, "maxeval"=maxeval, print_level=0));
     C[] <- res$solution;
@@ -732,19 +735,19 @@ EstimatorES <- function(...){
     if(any(res$objective==c(1e+100,1e+300))){
         # Reset the smoothing parameters
         j <- 1;
-        C[j] <- max(0.1,CLower[j]);
+        C[j] <- max(0,CLower[j]);
         if(Ttype!="N"){
             j <- j+1;
-            C[j] <- max(0.05,CLower[j]);
+            C[j] <- max(0,CLower[j]);
             if(Stype!="N"){
                 j <- j+1;
-                C[j] <- max(0.1,CLower[j]);
+                C[j] <- max(0,CLower[j]);
             }
         }
         else{
             if(Stype!="N"){
                 j <- j+1;
-                C[j] <- max(0.05,CLower[j]);
+                C[j] <- max(0,CLower[j]);
             }
         }
 
@@ -767,7 +770,7 @@ EstimatorES <- function(...){
         C[] <- res$solution;
     }
     # Change C if it is out of the bounds
-    if(any((C>=CUpper),(C<=CLower))){
+    if(any((C>CUpper),(C<CLower))){
         CUpper[C>=CUpper & C<0] <- C[C>=CUpper & C<0] * 0.999 + 0.001;
         CUpper[C>=CUpper & C>=0] <- C[C>=CUpper & C>=0] * 1.001 + 0.001;
         CLower[C<=CLower & C<0] <- C[C<=CLower & C<0] * 1.001 - 0.001;
@@ -803,6 +806,15 @@ EstimatorES <- function(...){
     # Parameters estimated + variance
     nParam <- length(C) + 1*(!rounded);
 
+    # Write down Fisher Information if needed
+    if(FI){
+        boundOriginal <- bounds;
+        bounds[] <- "n";
+        environment(likelihoodFunction) <- environment();
+        FI <- -numDeriv::hessian(likelihoodFunction,C);
+        bounds <- boundOriginal;
+    }
+
     # Check if smoothing parameters and phi reached the boundary conditions
     if(bounds=="u"){
         CNamesAvailable <- c("alpha","beta","gamma","phi")[c("alpha","beta","gamma","phi") %in% names(C)];
@@ -1043,7 +1055,7 @@ PoolPreparerES <- function(...){
 
                 listToReturn <- list(Etype=Etype,Ttype=Ttype,Stype=Stype,damped=damped,phi=phi,
                                      cfObjective=res$objective,C=res$C,ICs=res$ICs,icBest=NULL,
-                                     nParam=res$nParam,logLik=res$logLik,xreg=xreg,
+                                     nParam=res$nParam,logLik=res$logLik,xreg=xreg,FI=res$FI,
                                      xregNames=xregNames,matFX=matFX,vecgX=vecgX,nExovars=nExovars);
 
                 if(xregDo!="u"){
@@ -1201,7 +1213,7 @@ PoolEstimatorES <- function(silent=FALSE,...){
 
         listToReturn <- list(Etype=Etype,Ttype=Ttype,Stype=Stype,damped=damped,phi=phi,
                              cfObjective=res$objective,C=res$C,ICs=res$ICs,icBest=NULL,
-                             nParam=res$nParam,logLik=res$logLik,xreg=xreg,
+                             nParam=res$nParam,logLik=res$logLik,xreg=xreg, FI=res$FI,
                              xregNames=xregNames,matFX=matFX,vecgX=vecgX,nExovars=nExovars);
         if(xregDo!="u"){
             listToReturn <- XregSelector(listToReturn=listToReturn);
@@ -1260,7 +1272,7 @@ CreatorES <- function(silent=FALSE,...){
         res <- EstimatorES(ParentEnvironment=environment());
         listToReturn <- list(Etype=Etype,Ttype=Ttype,Stype=Stype,damped=damped,phi=phi,
                              cfObjective=res$objective,C=res$C,ICs=res$ICs,icBest=res$ICs,
-                             nParam=res$nParam,FI=FI,logLik=res$logLik,xreg=xreg,
+                             nParam=res$nParam,FI=res$FI,logLik=res$logLik,xreg=xreg,
                              xregNames=xregNames,matFX=matFX,vecgX=vecgX,nExovars=nExovars);
 
         if(xregDo!="u"){
@@ -1676,7 +1688,7 @@ CreatorES <- function(silent=FALSE,...){
         }
         else{
             if(!any(loss==c("MSE","MAE","HAM","MSEh","MAEh","HAMh","MSCE","MACE","CHAM",
-                              "TFL","aTFL","Rounded","TSB","LogisticD","LogisticL"))){
+                              "GPL","aGPL","Rounded","TSB","LogisticD","LogisticL"))){
                 if(modelDo=="combine"){
                     warning(paste0("'",loss,"' is used as loss function instead of 'MSE'.",
                                    "The produced combination weights may be wrong."),call.=FALSE);
@@ -1910,12 +1922,6 @@ CreatorES <- function(silent=FALSE,...){
             model <- paste0(Etype,Ttype,Stype);
         }
 
-# Write down Fisher Information if needed
-        if(FI){
-            environment(likelihoodFunction) <- environment();
-            FI <- -numDeriv::hessian(likelihoodFunction,C);
-        }
-
         ssFitter(ParentEnvironment=environment());
         # If this was rounded values, extract the variance
         if(rounded){