version 2.1.1

cran · Sep 5, 2017 · 330abf2 · 330abf2
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: smooth
 Type: Package
 Title: Forecasting Using Smoothing Functions
-Version: 2.1.0
-Date: 2017-08-01
+Version: 2.1.1
+Date: 2017-09-04
 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: 2017-08-01 09:37:05 UTC; isvetunkov
+Packaged: 2017-09-04 09:50:55 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: 2017-08-01 11:02:54 UTC
+Date/Publication: 2017-09-05 07:26:46 UTC
diff --git a/MD5 b/MD5
@@ -1,90 +1,90 @@
-cde30e3f336cfeef7095f108d07c53fe *DESCRIPTION
-10a13f9faa7c05f308c4572cc7e9244b *NAMESPACE
-761d2dea947ec0300ed7937ac65a536f *NEWS
+7568887096f933e5daa252b35519c806 *DESCRIPTION
+79c62e13c8bac7c5e75cdc11b2d543a6 *NAMESPACE
+6d4881fe57eace3eba82733a75274e67 *NEWS
 37349dc582f54f288b2894117ad36463 *R/RcppExports.R
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 43c220a1c003d0dbf0deb9abee04c8a5 *man/sowhat.Rd
-896b73b04105e548065f059a4e2e3ae6 *man/ssarima.Rd
+e4b22c2366b6fd0ad489e20ca3b21428 *man/ssarima.Rd
 731ca80b5424794abf9c17ec7a1612da *man/stepwise.Rd
-612e49a7cb2d268d537b2393d9d91075 *man/ves.Rd
-037b2c68c2d119c33ea1ae400bf7dbe0 *man/xregExpander.Rd
+83ea5f21159ca3571816961124a19ebf *man/ves.Rd
+bc1c0a7cd81c16a8f24ac5a09018d621 *man/xregExpander.Rd
 9859afefaf6500832d7469cadbbb28c8 *src/Makevars
 a6850c2998c396b505104b800670480e *src/Makevars.win
 d110340e8c48ffeb684cbd4b16102480 *src/RcppExports.cpp
 9fb0915a4e7ffe192150808e5e890c69 *src/registerDynamicSymbol.c
-589d540c08eccecd0b16e745fe3bcd96 *src/ssgeneralfun.cpp
+71289eef1d91e05b6fb33a72d87a42bb *src/ssgeneralfun.cpp
 43ce084a9895aa52735c4e427cca2779 *src/vssGeneral.cpp
 4e0f43b23ba7abbb29b225614e88f276 *tests/testthat.R
 b8cb94b5355e771a3f6415f5b349c059 *tests/testthat/test_ces.R
@@ -93,7 +93,7 @@ a19ef0fe56d3676306870de9d229232b *tests/testthat/test_ges.R
 2e692854ad815da62cc381b7e53fdba5 *tests/testthat/test_iss.R
 44a29130669ac4b28e2e61d497576101 *tests/testthat/test_simulate.R
 d168343e2d2c516102191e5f0e48cb44 *tests/testthat/test_ssarima.R
-3baf7d9a5ca4424a2d0983c038f49981 *tests/testthat/test_ves.R
+c9bc7f36d491e62c70cd1344ff6bbcf0 *tests/testthat/test_ves.R
 1ceeb5bf5e782cc865cceea1e1b4b4f4 *vignettes/ces.Rmd
 06619b9025a97f09332d42c0279219cf *vignettes/es.Rmd
 78564d21b17ab88ef7756a729df871be *vignettes/ges.Rmd

diff --git a/NAMESPACE b/NAMESPACE
@@ -15,6 +15,8 @@ S3method(logLik,smooth.sim)
 S3method(modelType,default)
 S3method(modelType,vsmooth)
 S3method(nParam,default)
+S3method(nParam,iss)
+S3method(nParam,smooth)
 S3method(nobs,iss)
 S3method(nobs,smooth)
 S3method(nobs,smooth.sim)
@@ -114,6 +116,7 @@ importFrom(stats,optim)
 importFrom(stats,optimize)
 importFrom(stats,plnorm)
 importFrom(stats,pnorm)
+importFrom(stats,qchisq)
 importFrom(stats,qlnorm)
 importFrom(stats,qnorm)
 importFrom(stats,qt)

diff --git a/NEWS b/NEWS
@@ -1,3 +1,25 @@
+smooth v2.1.1 (Release data: 2017-09-04)
+==============
+
+Changes:
+* initials for parameters of AR now take into account the number of parameters. Should simplify optimisation of AR.
+* Number of parameters in univariate models now consists of the part "estimated" and "provided". Each model now returns a table as nParam with a detailed info about what was estimated/provided. If something was provided, then it is no longer taken into account in calculation of statistics (i.e. AIC, sigma etc).
+* Similar thing is now implemented for ves().
+* ves() now can produce "independent" prediction intervals (when covariances for time series are ignored).
+* Yet another change in the number of parameters to estimate in es(), ges() and ces(), taking that some values could be provided, while the others - left for optimisation.
+* sma() now restricts the maximum order in case of order selection to 200. This corresponds to the case with global level for different time series and should be sufficient in 99.9% of cases.
+
+Bugfixes:
+* auto.ssarima() had a bug with dataMA.
+* auto.ssarima() did not calculate correctly number of iterations for AR models.
+* Several minor bugfixes in ges() and ssarima() functions (bugs with provided values).
+* Because of R's unique feature of working with names of variables, the situation when CUpper is provided, but C is not, was not handled well.
+* Check of maximum number of parameters to estimate did not take into account some of the provided values.
+* sma() wasn't checking what is provided as order. Now it does.
+* auto.ssarima() did not estimated percentage of estimated models correctly in cases of constant!=NULL.
+* Cumulative variance was calculated incorrectly. Now the non-seasonal case is fixed. SEASONAL MODELS STILL HAVE THIS PROBLEM!
+* Found bug in variance calculation for some seasonal models, which caused intervals for SSARIMA, GES and CES to be narrower than needed.
+
 smooth v2.1.0 (Release data: 2017-08-01)
 ==============
 
@@ -7,7 +29,7 @@ Changes:
 * es() now accepts model estimated using ets() function from forecast package.
 * Probability in TSB is now restricted with [0, 1] region.
 * We can now fit es() even on 3 observations, but with persistence forced to be equal to zero. Don't expect a good model, though...
-* auto.ssarima() now allows defining whether if constant is needed or not. By default it will check this automatically.
+* auto.ssarima() now allows defining whether constant is needed or not. By default it will check this automatically.
 * Initials of xreg before the optimisation are now based on OLS estimates.
 * In case of updateX=TRUE, we now check a very basic forecastability condition.
 * A couple of new examples in vignettes.

diff --git a/R/autossarima.R b/R/autossarima.R
@@ -232,7 +232,8 @@ auto.ssarima <- function(data, orders=list(ar=c(3,3),i=c(2,1),ma=c(3,3)), lags=c
     lags <- sort(lags,decreasing=FALSE);
 
 # 1 stands for constant, the other one stands for variance
-    nParamMax <- max(ar.max %*% lags + i.max %*% lags,ma.max %*% lags) + sum(ar.max) + sum(ma.max) + constantCheck + 1;
+    nParamMax <- (1 + max(ar.max %*% lags + i.max %*% lags,ma.max %*% lags)
+                  + sum(ar.max) + sum(ma.max) + constantCheck);
 
 # Try to figure out if the number of parameters can be tuned in order to fit something smaller on small samples
 # Don't try to fix anything if the number of seasonalities is greater than 2
@@ -289,7 +290,12 @@ auto.ssarima <- function(data, orders=list(ar=c(3,3),i=c(2,1),ma=c(3,3)), lags=c
     }
 
 # 1 stands for constant/no constant, another one stands for ARIMA(0,0,0)
-    nModels <- prod(i.max + 1) * (1 + sum(ma.max*(1 + sum(ar.max)))) + 1;
+    if(all(ma.max==0)){
+        nModels <- prod(i.max + 1) * (1 + sum(ar.max)) + constantCheck;
+    }
+    else{
+        nModels <- prod(i.max + 1) * (1 + sum(ma.max*(1 + sum(ar.max)))) + constantCheck;
+    }
     testModel <- list(NA);
 # Array with elements x maxorders x horizon x point/lower/upper
     if(combine){
@@ -399,14 +405,14 @@ auto.ssarima <- function(data, orders=list(ar=c(3,3),i=c(2,1),ma=c(3,3)), lags=c
         }
         if(m==1){
             bestIC <- ICValue;
-            dataI <- testModel$residuals;
+            dataMA <- dataI <- testModel$residuals;
             i.best <- i.orders[d,];
             bestICAR <- bestICI <- bestICMA <- bestIC;
         }
         else{
             if(ICValue < bestICI){
                 bestICI <- ICValue;
-                dataI <- testModel$residuals;
+                dataMA <- dataI <- testModel$residuals;
                 if(ICValue < bestIC){
                     i.best <- i.orders[d,];
                     bestIC <- ICValue;
@@ -579,7 +585,7 @@ auto.ssarima <- function(data, orders=list(ar=c(3,3),i=c(2,1),ma=c(3,3)), lags=c
                             if(silent[1]=="d"){
                                 cat("AR: ");cat(ar.test);cat(", ");
                             }
-                            testModel <- ssarima(dataI, orders=list(ar=ar.test,i=0,ma=0), lags=lags,
+                            testModel <- ssarima(dataMA, orders=list(ar=ar.test,i=0,ma=0), lags=lags,
                                                  constant=FALSE, initial=initialType, cfType=cfType,
                                                  h=h, holdout=FALSE,
                                                  intervals=intervals, level=level,
@@ -630,14 +636,14 @@ auto.ssarima <- function(data, orders=list(ar=c(3,3),i=c(2,1),ma=c(3,3)), lags=c
         }
     }
 
-    m <- m + 1;
-    if(silentText==FALSE){
-        cat(paste0(rep("\b",nchar(round(m/nModels,2)*100)+1),collapse=""));
-        cat(paste0(round((m)/nModels,2)*100,"%"));
-    }
-
 #### Test the constant ####
     if(constantCheck){
+        m <- m + 1;
+        if(silentText==FALSE){
+            cat(paste0(rep("\b",nchar(round(m/nModels,2)*100)+1),collapse=""));
+            cat(paste0(round((m)/nModels,2)*100,"%"));
+        }
+
         if(any(c(ar.best,i.best,ma.best)!=0)){
             testModel <- ssarima(data, orders=list(ar=(ar.best),i=(i.best),ma=(ma.best)), lags=(lags),
                                  constant=FALSE, initial=initialType, cfType=cfType,