Merge remote-tracking branch 'upstream/master' into start-smooth-for-python

Author: ltsaprounis

.github/workflows/test.yml

@@ -33,13 +33,13 @@ jobs:
         uses: actions/checkout@v2
 
       - name: Setup R (${{ matrix.config.r }})
-        uses: r-lib/actions/setup-r@v1
+        uses: r-lib/actions/setup-r@v2
         with:
           r-version: ${{ matrix.config.r }}
           http-user-agent: ${{ matrix.config.http-user-agent }}
 
       - name: Setup Pandoc
-        uses: r-lib/actions/setup-pandoc@v1
+        uses: r-lib/actions/setup-pandoc@v2
 
       - name: Query dependencies
         run: |

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 3.2.0
-Date: 2023-01-17 22:46:37 UTC
-SHA: 1e1609ec5c9faffd021b14d3709bf182af4b9801
+Version: 4.0.0
+Date: 2023-09-16 10:25:42 UTC
+SHA: 93d2e3fa4bded6ac4faee02dd50694428bb1fac6

DESCRIPTION

@@ -1,25 +1,25 @@
 Package: smooth
 Type: Package
 Title: Forecasting Using State Space Models
-Version: 3.2.1.41001
-Date: 2023-01-18
+Version: 4.0.0
+Date: 2023-09-15
 Authors@R: person("Ivan", "Svetunkov", email = "[email protected]", role = c("aut", "cre"),
                   comment="Lecturer at Centre for Marketing Analytics and Forecasting, Lancaster University, UK")
 URL: https://github.com/config-i1/smooth
 BugReports: https://github.com/config-i1/smooth/issues
 Language: en-GB
 Description: Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting.
-             The package includes ADAM (Svetunkov, 2021, <https://openforecast.org/adam/>),
+             The package includes ADAM (Svetunkov, 2023, <https://openforecast.org/adam/>),
              Exponential Smoothing (Hyndman et al., 2008, <doi: 10.1007/978-3-540-71918-2>),
              SARIMA (Svetunkov & Boylan, 2019 <doi: 10.1080/00207543.2019.1600764>),
              Complex Exponential Smoothing (Svetunkov & Kourentzes, 2018, <doi: 10.13140/RG.2.2.24986.29123>),
              Simple Moving Average (Svetunkov & Petropoulos, 2018 <doi: 10.1080/00207543.2017.1380326>)
              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)
+License: LGPL-2.1
 Depends:
     R (>= 3.0.2),
-    greybox (>= 1.0.7)
+    greybox (>= 1.0.8)
 Imports:
     Rcpp (>= 0.12.3),
     stats,
@@ -44,6 +44,6 @@ Suggests:
     doParallel,
     foreach
 VignetteBuilder: knitr
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
 Encoding: UTF-8
 Roxygen: list(old_usage = TRUE)

NAMESPACE

@@ -2,6 +2,8 @@
 
 S3method(AICc,smooth)
 S3method(BICc,smooth)
+S3method(accuracy,smooth)
+S3method(accuracy,smooth.forecast)
 S3method(actuals,adam)
 S3method(actuals,msdecompose)
 S3method(actuals,smooth)
@@ -74,6 +76,7 @@ S3method(plot,msdecompose)
 S3method(plot,oes)
 S3method(plot,oes.sim)
 S3method(plot,reapply)
+S3method(plot,reapplyCombined)
 S3method(plot,smooth)
 S3method(plot,smooth.forecast)
 S3method(plot,smooth.sim)
@@ -92,11 +95,13 @@ S3method(print,msdecompose)
 S3method(print,oes)
 S3method(print,oes.sim)
 S3method(print,reapply)
+S3method(print,reapplyCombined)
 S3method(print,smooth)
 S3method(print,smooth.forecast)
 S3method(print,smooth.sim)
 S3method(print,summary.adam)
 S3method(reapply,adam)
+S3method(reapply,adamCombined)
 S3method(reapply,default)
 S3method(reforecast,adam)
 S3method(reforecast,default)
@@ -123,6 +128,7 @@ S3method(summary,smooth.forecast)
 S3method(vcov,adam)
 S3method(xtable,adam)
 S3method(xtable,summary.adam)
+export(accuracy)
 export(adam)
 export(auto.adam)
 export(auto.ces)
@@ -175,6 +181,7 @@ export(xtable)
 import(Rcpp)
 import(zoo)
 importFrom(MASS,mvrnorm)
+importFrom(generics,accuracy)
 importFrom(generics,forecast)
 importFrom(grDevices,dev.interactive)
 importFrom(grDevices,devAskNewPage)

NEWS

@@ -1,8 +1,46 @@
-smooth v3.2.1 (Release data: 2023-01-18)
+smooth v4.0.0 (Release data: 2023-09-15)
 =======
 
+Changes:
+* Starting from v4.0.0, the smooth package for R will be released under the LGPLv2.1 license. The source of the older version of the software under the GPL(>=2) is available here: https://github.com/config-i1/smooth/releases/tag/v3.2.2
+
+
+smooth v3.2.2 (Release data: 2023-09-15)
+=======
+
+Changes:
+* Expanded info on simulate functions in the package description.
+* vcov() now can also return heuristic covariance matrix for adam via the heuristics=0.0025 or something like that. This is an experimental feature, which is why it is not explained in the documentation.
+* auto.msarima() is now a wrapper of adam() with orders=list(..., select=TRUE).
+* accuracy() is now implemented for smooth classes.
+
+Bugfixes:
+* forecast.adam() would not work correctly if the newdata had missing values.
+* Finally found and fixed an annoying bug that caused ARIMA to fail in some cases.
+* Fix in forecast.adam() for cases of h=1 and analytical variance.
+* A fix for adam() with LASSO/RIDGE, where "lambdaOriginal was not found".
+
+
+smooth v3.2.1 (Release data: 2023-06-01)
+=======
+
+Changes:
+* Combined ADAM now skips the models with weights less than 0.001, when producing forecasts.
+* forecast() for combined ADAM now also supports interval="complete".
+* reapply() method for combined ADAM.
+
 Bugfixes:
 * Correct default for initial in auto.adam().
+* PACF in plot.smooth() dropped the first lag because of a typo.
+* adam() would not calculate logLik() correctly for Generalised Normal distribution in case of Multiplicative error models.
+* sma() with order selection would not work on data with number of observations lower than the frequency.
+* Fix in simulate.adam(), which would ignore the states of the model.
+* sm() now works with pure regression and regressors="select". But we now rely on greybox 1.0.8.
+* formula is now properly used in adam() in regressors="select".
+* smoothCombine() wouldn't work because of the wrong interval type.
+* coefbootstrap() now has a slightly larger smallest sample to consider (1 more observation). This should make it more robust.
+* Fix in adam() with Gamma distribution and mixed models.
+* Fixed a bug with ARIMA(0,d,0) with drift and an initial B becoming outside of bounds.
 
 
 smooth v3.2.0 (Release data: 2023-01-18)

R/adam-es.R

@@ -78,6 +78,10 @@
 #' Keep in mind that model selection with "Z" components uses Branch and Bound
 #' algorithm and may skip some models that could have slightly smaller
 #' information criteria.
+#' @param lags Defines lags for the corresponding components. All components
+#' count, starting from level, so ETS(M,M,M) model for monthly data will have
+#' \code{lags=c(1,1,12)}. However, the function will also accept \code{lags=c(12)},
+#' assuming that the lags 1 were dropped.
 #' @param phi Value of damping parameter. If \code{NULL} then it is estimated.
 #' @param initial Can be either character or a vector of initial states.
 #' If it is character, then it can be \code{"optimal"}, meaning that all initial
@@ -225,7 +229,7 @@
 #'
 #' @rdname es
 #' @export
-es <- function(y, model="ZZZ", persistence=NULL, phi=NULL,
+es <- function(y, model="ZZZ", lags=c(frequency(y)), persistence=NULL, phi=NULL,
                initial=c("optimal","backcasting","complete"), initialSeason=NULL, ic=c("AICc","AIC","BIC","BICc"),
                loss=c("likelihood","MSE","MAE","HAM","MSEh","TMSE","GTMSE","MSCE"),
                h=10, holdout=FALSE,
@@ -354,7 +358,7 @@ es <- function(y, model="ZZZ", persistence=NULL, phi=NULL,
         data <- ts(data, start=start(y), frequency=frequency(y));
         colnames(data)[1] <- "y";
         # Give name to the explanatory variables if they do not have them
-        if(is.null(names(xreg))){
+        if(is.null(colnames(xreg))){
             if(!is.null(ncol(xreg))){
                 colnames(data)[-1] <- paste0("x",c(1:ncol(xreg)));
             }
@@ -397,7 +401,7 @@ es <- function(y, model="ZZZ", persistence=NULL, phi=NULL,
                 "Please use forecast() method to produce cumulative values.")
     }
 
-    ourModel <- adam(data=data, model=model, persistence=persistence, phi=phi,
+    ourModel <- adam(data=data, model=model, lags=lags, persistence=persistence, phi=phi,
                      loss=loss, h=h, holdout=holdout, initial=initialValue,
                      ic=ic, bounds=bounds, distribution="dnorm",
                      silent=silent, regressors=regressors[1], ...);

R/adam-msarima.R

@@ -197,9 +197,9 @@ msarima <- function(y, orders=list(ar=c(0),i=c(1),ma=c(1)), lags=c(1),
                     h=10, holdout=FALSE,
                     # cumulative=FALSE,
                     # interval=c("none","parametric","likelihood","semiparametric","nonparametric"), level=0.95,
-                    bounds=c("admissible","none"),
+                    bounds=c("usual","admissible","none"),
                     silent=TRUE,
-                    xreg=NULL, regressors=c("use","select"), initialX=NULL, ...){
+                    xreg=NULL, regressors=c("use","select","adapt"), initialX=NULL, ...){
     # Copyright (C) 2022 - Inf  Ivan Svetunkov
 
     # Start measuring the time of calculations

R/adam-sma.R

@@ -285,8 +285,8 @@ sma <- function(y, order=NULL, ic=c("AICc","AIC","BIC","BICc"),
                 # If the new IC is the same as one of the old ones, stop
                 optimalICNotFound[] <- j!=i && j!=k && j!=0;
             }
-            # Check a specifif order equal to frequency of the data
-            if(is.na(ICs[yFrequency])){
+            # Check a specific order equal to frequency of the data
+            if(is.na(ICs[yFrequency]) && obsInSample>=yFrequency){
                 ICs[yFrequency] <- CreatorSMA(yFrequency);
                 if(!silent){
                     cat(paste0("Order " , yFrequency, " - ", round(ICs[yFrequency],4), "\n"));