version 2.0.0

cran · Jul 2, 2017 · a197924 · a197924
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: smooth
 Type: Package
 Title: Forecasting Using Smoothing Functions
-Version: 1.9.9
-Date: 2017-05-03
+Version: 2.0.0
+Date: 2017-07-03
 Authors@R: person("Ivan", "Svetunkov", email = "ivan@svetunkov.ru", role = c("aut", "cre"),
                   comment="Research Associate at Lancaster Centre for Forecasting, UK")
 URL: https://github.com/config-i1/smooth
@@ -19,9 +19,9 @@ Suggests: Mcomp, numDeriv, testthat, knitr, rmarkdown
 VignetteBuilder: knitr
 RoxygenNote: 6.0.1
 NeedsCompilation: yes
-Packaged: 2017-05-02 22:22:47 UTC; config
+Packaged: 2017-07-02 14:17:43 UTC; isvetunkov
 Author: Ivan Svetunkov [aut, cre] (Research Associate at Lancaster Centre for
     Forecasting, UK)
 Maintainer: Ivan Svetunkov <ivan@svetunkov.ru>
 Repository: CRAN
-Date/Publication: 2017-05-03 06:50:01 UTC
+Date/Publication: 2017-07-02 22:54:47 UTC
diff --git a/MD5 b/MD5
@@ -1,95 +1,104 @@
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diff --git a/NAMESPACE b/NAMESPACE
@@ -2,6 +2,7 @@
 
 S3method(AICc,default)
 S3method(coef,smooth)
+S3method(coef,vsmooth)
 S3method(fitted,smooth)
 S3method(forecast,smooth)
 S3method(getResponse,smooth)
@@ -11,24 +12,33 @@ S3method(lags,default)
 S3method(logLik,iss)
 S3method(logLik,smooth)
 S3method(logLik,smooth.sim)
-S3method(model.type,default)
+S3method(modelType,default)
+S3method(modelType,vsmooth)
+S3method(nParam,default)
 S3method(nobs,iss)
 S3method(nobs,smooth)
 S3method(nobs,smooth.sim)
+S3method(nobs,vsmooth)
 S3method(orders,Arima)
 S3method(orders,default)
 S3method(plot,iss)
 S3method(plot,smooth)
 S3method(plot,smooth.forecast)
 S3method(plot,smooth.sim)
+S3method(pointLik,default)
+S3method(pointLik,smooth)
 S3method(print,iss)
 S3method(print,smooth)
 S3method(print,smooth.forecast)
 S3method(print,smooth.sim)
+S3method(print,vsmooth)
+S3method(sigma,smooth)
+S3method(sigma,vsmooth)
 S3method(simulate,smooth)
 S3method(summary,iss)
 S3method(summary,smooth)
 S3method(summary,smooth.forecast)
+S3method(summary,vsmooth)
 export(AICc)
 export(MAPE)
 export(MASE)
@@ -49,8 +59,11 @@ export(hm)
 export(iss)
 export(lags)
 export(model.type)
+export(modelType)
+export(nParam)
 export(orders)
 export(pls)
+export(pointLik)
 export(sCE)
 export(sMSE)
 export(sPIS)
@@ -62,6 +75,7 @@ export(sma)
 export(sowhat)
 export(ssarima)
 export(stepwise)
+export(ves)
 export(xregExpander)
 import(Rcpp)
 import(zoo)
@@ -80,11 +94,14 @@ importFrom(stats,AIC)
 importFrom(stats,BIC)
 importFrom(stats,as.formula)
 importFrom(stats,coef)
+importFrom(stats,coefficients)
 importFrom(stats,cor)
 importFrom(stats,cov)
 importFrom(stats,dbeta)
 importFrom(stats,decompose)
 importFrom(stats,deltat)
+importFrom(stats,dlnorm)
+importFrom(stats,dnorm)
 importFrom(stats,end)
 importFrom(stats,frequency)
 importFrom(stats,is.ts)
@@ -107,10 +124,12 @@ importFrom(stats,rlnorm)
 importFrom(stats,rnorm)
 importFrom(stats,rt)
 importFrom(stats,runif)
+importFrom(stats,sigma)
 importFrom(stats,simulate)
 importFrom(stats,start)
 importFrom(stats,time)
 importFrom(stats,ts)
 importFrom(stats,var)
+importFrom(stats,window)
 importFrom(utils,packageVersion)
-useDynLib(smooth, .registration=TRUE)
+useDynLib(smooth)
diff --git a/NEWS b/NEWS
@@ -1,3 +1,30 @@
+smooth v2.0.0 (Release data: 2017-07-01)
+==============
+
+Changes:
+* New function - ves(). Currently the basic features of all the pure additive and pure multiplicative models are implemented. No prediction intervals, no intermittence, no automatic model selection.
+* New parameter - imodel, which determines the type of model for probability in case of iSS models. Can also accept models previously estimated using iss() function.
+* Point forecasts in case of simulated data are now based on the simulation rather than analytical expression.
+* Max order of SMA is now equal to obsInSample.
+* Introduced a hidden parameter, rounded, which determines whether the rounded up value of demand sizes is assumed in the model. This influences the cost function and leads to a different results in case of intermittent model.
+* If cumulative==TRUE, then we now return cumulative holdout values as well.
+* New method pointLik() for smooth class. It returns the vector of log-likelihoods for each separate observation.
+* Better integration of sim.es() objects and es() function. Now you can do something like this to fit the "true" model: x <- sim.es("MNN"); es(x$data,model=x).
+* es() now also returns transition matrix.
+* Optimised C++ code, which should result in the growth of computational speed for ~25%.
+* es() now returns PLS value in the accuracy for all the types of models.
+
+Bugfixes:
+* simulator function in Rcpp did not cover the cases, when level could become negative. This is now fixed.
+* ssarima() now checks if the number of observations is enough for the model and stops if it is not.
+* es() didn't work in some cases of seasonal models, when number of in-sample observations was less than twice frequency of data. The seasonal model should not be used there in the first place.
+* getResponse() function now returns the in-sample actuals.
+* exists() functions in the code would look into the parent environments. Now they don't.
+* sim.ssarima() returned state vector with wrong names in case of constant.
+* When coef() was applied to es() objects, the initialSeason parameters were not returned because of the old name of the variable.
+* Corrected a problem with initials of iETSX models.
+
+
 smooth v1.9.9 (Release data: 2017-05-03)
 ==============
 

diff --git a/R/RcppExports.R b/R/RcppExports.R
@@ -45,7 +45,19 @@ costfuncARIMA <- function(ARorders, MAorders, Iorders, ARIMAlags, nComp, AR, MA,
     .Call('smooth_costfuncARIMA', PACKAGE = 'smooth', ARorders, MAorders, Iorders, ARIMAlags, nComp, AR, MA, constant, Cvalues, matvt, matF, matw, yt, vecg, h, modellags, Etype, Ttype, Stype, multisteps, CFt, normalizer, fittertype, nexovars, matxt, matat, matFX, vecgX, ot, estimAR, estimMA, requireConst, estimConst, estimxreg, gowild, estimFX, estimgX, estiminitX, bounds)
 }
 
-simulatorwrap <- function(arrvt, materrors, matot, matF, matw, matg, Etype, Ttype, Stype, modellags) {
-    .Call('smooth_simulatorwrap', PACKAGE = 'smooth', arrvt, materrors, matot, matF, matw, matg, Etype, Ttype, Stype, modellags)
+simulatorwrap <- function(arrvt, matErrors, matot, matF, matw, matg, Etype, Ttype, Stype, modellags) {
+    .Call('smooth_simulatorwrap', PACKAGE = 'smooth', arrvt, matErrors, matot, matF, matw, matg, Etype, Ttype, Stype, modellags)
+}
+
+vFitterWrap <- function(yt, matvt, matF, matw, matG, modellags, Etype, Ttype, Stype, ot) {
+    .Call('smooth_vFitterWrap', PACKAGE = 'smooth', yt, matvt, matF, matw, matG, modellags, Etype, Ttype, Stype, ot)
+}
+
+vForecasterWrap <- function(matvt, matF, matw, series, h, Etype, Ttype, Stype, modellags) {
+    .Call('smooth_vForecasterWrap', PACKAGE = 'smooth', matvt, matF, matw, series, h, Etype, Ttype, Stype, modellags)
+}
+
+vOptimiserWrap <- function(yt, matvt, matF, matw, matG, modellags, Etype, Ttype, Stype, cfType, normalizer, bounds, ot) {
+    .Call('smooth_vOptimiserWrap', PACKAGE = 'smooth', yt, matvt, matF, matw, matG, modellags, Etype, Ttype, Stype, cfType, normalizer, bounds, ot)
 }
 
diff --git a/R/autoces.R b/R/autoces.R
@@ -59,7 +59,7 @@ auto.ces <- function(data, models=c("none","simple","full"),
                 cfType=c("MSE","MAE","HAM","GMSTFE","MSTFE","MSEh","TFL"),
                 h=10, holdout=FALSE, cumulative=FALSE,
                 intervals=c("none","parametric","semiparametric","nonparametric"), level=0.95,
-                intermittent=c("none","auto","fixed","croston","tsb","sba"),
+                intermittent=c("none","auto","fixed","croston","tsb","sba"), imodel="MNN",
                 bounds=c("admissible","none"),
                 silent=c("none","all","graph","legend","output"),
                 xreg=NULL, xregDo=c("use","select"), initialX=NULL,
@@ -79,7 +79,7 @@ auto.ces <- function(data, models=c("none","simple","full"),
 
 ##### Set environment for ssInput and make all the checks #####
     environment(ssAutoInput) <- environment();
-    ssAutoInput(modelType="ces",ParentEnvironment=environment());
+    ssAutoInput("auto.ces",ParentEnvironment=environment());
 
 # If the pool of models is wrong, fall back to default
     if(length(models)!=1){
@@ -96,16 +96,6 @@ auto.ces <- function(data, models=c("none","simple","full"),
     models <- substr(models,1,1);
 
     datafreq <- frequency(data);
-    # if(any(is.na(data))){
-    #     if(silentText==FALSE){
-    #         message("Data contains NAs. These observations will be excluded.")
-    #     }
-    #     datanew <- data[!is.na(data)];
-    #     if(is.ts(data)){
-    #         datanew <- ts(datanew,start=start(data),frequency=datafreq);
-    #     }
-    #     data <- datanew;
-    # }
 
     # Define maximum needed number of parameters
     if(any(models=="n")){
@@ -159,7 +149,7 @@ auto.ces <- function(data, models=c("none","simple","full"),
                         cfType=cfType,
                         h=h, holdout=holdout,cumulative=cumulative,
                         intervals=intervals, level=level,
-                        intermittent=intermittent,
+                        intermittent=intermittent, imodel=imodel,
                         bounds=bounds, silent=silent,
                         xreg=xreg, xregDo=xregDo, initialX=initialX,
                         updateX=updateX, persistenceX=persistenceX, transitionX=transitionX, FI=FI);
@@ -200,7 +190,7 @@ auto.ces <- function(data, models=c("none","simple","full"),
                              cfType=cfType,
                              h=h, holdout=holdout,cumulative=cumulative,
                              intervals=intervals, level=level,
-                             intermittent=intermittent,
+                             intermittent=intermittent, imodel=imodel,
                              bounds=bounds, silent=TRUE,
                              xreg=xreg, xregDo=xregDo, initialX=initialX,
                              updateX=updateX, persistenceX=persistenceX, transitionX=transitionX, FI=FI);
@@ -241,7 +231,7 @@ auto.ces <- function(data, models=c("none","simple","full"),
         }
         else{
             graphmaker(actuals=data,forecast=y.for.new,fitted=y.fit,
-                       level=level,legend=!silentLegend,main=modelname,cumulative=cumulative);
+                       legend=!silentLegend,main=modelname,cumulative=cumulative);
         }
     }