diff --git a/R/M3_example.R b/R/M3_example.R
new file mode 100644
index 0000000..1acd0d8
--- /dev/null
+++ b/R/M3_example.R
@@ -0,0 +1,10 @@
+#' Example of a time series from the M3 forecasting competition
+#'
+#' A monthly time series, from the M3 forecasting competition ("N1485").
+#'
+#' @format
+#' List of time series of class \link[stats]{ts}.
+#'
+#'
+#' @source [https://forecasters.org/resources/time-series-data/m3-competition/](https://forecasters.org/resources/time-series-data/m3-competition/)
+"M3_example"
\ No newline at end of file
diff --git a/R/carparts_example.R b/R/carparts_example.R
new file mode 100644
index 0000000..6c70c59
--- /dev/null
+++ b/R/carparts_example.R
@@ -0,0 +1,16 @@
+#' Example of a time series from carparts
+#'
+#' A monthly time series from the `carparts` dataset, 51 observations, Jan 1998 - Mar 2002.
+#'
+#' @format
+#' Univariate time series of class \link[stats]{ts}.
+#'
+#' @references
+#' Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D., (2008) Forecasting with exponential
+#' smoothing: the state space approach, Springer
+#'
+#' Godahewa, Rakshitha, Bergmeir, Christoph, Webb, Geoff, Hyndman, Rob, & Montero-Manso, Pablo. (2020). Car Parts Dataset (without Missing Values) (Version 2) \doi{10.5281/zenodo.4656021}
+#'
+#' @source
+#' Godahewa, Rakshitha, Bergmeir, Christoph, Webb, Geoff, Hyndman, Rob, & Montero-Manso, Pablo. (2020). Car Parts Dataset (without Missing Values) (Version 2) \doi{10.5281/zenodo.4656021}
+"carparts_example"
\ No newline at end of file
diff --git a/data/M3_example.rda b/data/M3_example.rda
new file mode 100644
index 0000000..1042b89
Binary files /dev/null and b/data/M3_example.rda differ
diff --git a/data/carparts_example.rda b/data/carparts_example.rda
new file mode 100644
index 0000000..f631dc9
Binary files /dev/null and b/data/carparts_example.rda differ
diff --git a/man/M3_example.Rd b/man/M3_example.Rd
new file mode 100644
index 0000000..b28d993
--- /dev/null
+++ b/man/M3_example.Rd
@@ -0,0 +1,19 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/M3_example.R
+\docType{data}
+\name{M3_example}
+\alias{M3_example}
+\title{Example of a time series from the M3 forecasting competition}
+\format{
+List of time series of class \link[stats]{ts}.
+}
+\source{
+\url{https://forecasters.org/resources/time-series-data/m3-competition/}
+}
+\usage{
+M3_example
+}
+\description{
+A monthly time series, from the M3 forecasting competition ("N1485").
+}
+\keyword{datasets}
diff --git a/man/carpart.Rd b/man/carparts_example.Rd
similarity index 82%
rename from man/carpart.Rd
rename to man/carparts_example.Rd
index e7f1756..0b84b5c 100644
--- a/man/carpart.Rd
+++ b/man/carparts_example.Rd
@@ -1,9 +1,9 @@
% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/carpart.R
+% Please edit documentation in R/carparts_example.R
\docType{data}
-\name{carpart}
-\alias{carpart}
-\title{Carpart time series}
+\name{carparts_example}
+\alias{carparts_example}
+\title{Example of a time series from carparts}
\format{
Univariate time series of class \link[stats]{ts}.
}
@@ -11,7 +11,7 @@ Univariate time series of class \link[stats]{ts}.
Godahewa, Rakshitha, Bergmeir, Christoph, Webb, Geoff, Hyndman, Rob, & Montero-Manso, Pablo. (2020). Car Parts Dataset (without Missing Values) (Version 2) \doi{10.5281/zenodo.4656021}
}
\usage{
-carpart
+carparts_example
}
\description{
A monthly time series from the \code{carparts} dataset, 51 observations, Jan 1998 - Mar 2002.
diff --git a/vignettes/bayesRecon.Rmd b/vignettes/bayesRecon.Rmd
index 044b207..06b0a58 100644
--- a/vignettes/bayesRecon.Rmd
+++ b/vignettes/bayesRecon.Rmd
@@ -20,7 +20,8 @@ vignette: >
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
- eval=TRUE ### !!!! set to FALSE here to render only the text !!!!
+ #eval=TRUE ### !!!! set to FALSE here to render only the text !!!!
+ eval=FALSE ### !!!! set to FALSE here to render only the text !!!!
)
set.seed(42)
```
@@ -34,28 +35,17 @@ klippy::klippy(position = c('top', 'right'), tooltip_message = 'Copy', tooltip_s
This vignette shows how to perform *probabilistic reconciliation* with
the `bayesRecon` package. We provide three examples:
-1. *Temporal hierarchy for a count time series*: we build a temporal hierarchy over a count time series, produce base forecasts using glarma and reconcile them via Bottom-Up Importance Sampling (BUIS).
+1. *Temporal hierarchy for a count time series*: we build a temporal hierarchy over a count time series, produce the base forecasts using `glarma` and reconcile them via Bottom-Up Importance Sampling (BUIS).
-2. *Temporal hierarchy for a smooth time series*: we build a temporal hierarchy over a smooth time series, compute the base forecasts using ets and we reconcile them in closed form using Gaussian reconciliation.
+2. *Temporal hierarchy for a smooth time series*: we build a temporal hierarchy over a smooth time series, compute the base forecasts using `ets` and we reconcile them in closed form using Gaussian reconciliation.
-3. *Cross-sectional hierarchy of smooth time series*: we reconcile forecasts for a cross-sectional hierarchy. Also in this case we use ets to generate the base forecasts and we apply Gaussian reconciliation.
+3. *Hierarchical of smooth time series*: this is an example of a cross-sectional hierarchy. We generate the base forecasts using `ets` and we reconcile them via Gaussian reconciliation.
4. *Cross-sectional hierarchy of count time series*
LZ
toglierei le citazioni
-The reconciliation algorithms are discussed in:
-
-* Zambon, L., Azzimonti, D., & Corani, G. (2022). Efficient probabilistic reconciliation of forecasts for real-valued and count time series,
-Statistics and Computing 34.1 (2024): 21.
-
-* Zambon, L., Agosto, A., Giudici, P., & Corani, G. (2023). Properties of the reconciled distributions for Gaussian and count forecasts, arXiv preprint arXiv:2303.15135.
-
-* Corani, G., Azzimonti, D., & Rubattu, N. (2023). Probabilistic reconciliation of count time series, International Journal of Forecasting (2023).
-
-* Corani, G., Azzimonti, D., Augusto, J. P., & Zaffalon, M. (2021). Probabilistic reconciliation of hierarchical forecast via Bayes' rule.
-Proc. ECML-PKDD 2020, pp. 211-226.
# Installation
@@ -75,7 +65,7 @@ library(bayesRecon)
We select a monthly time series of counts from the *carparts* dataset
citerei piuttosto il package expsmooth [@expsmooth_pkg]
[@hyndman2008forecasting].
-The data set contains time series of sales of cars part from Jan. 1998 to Mar. 2002. We select in particular time series #2655, which we make it available as `bayesRecon::carpart`.
+The data set contains time series of sales of cars part from Jan. 1998 to Mar. 2002. We select in particular time series #2655, which we make it available as `bayesRecon::carparts_example`.
la chiamerei piuttosto carparts_example
@@ -83,15 +73,15 @@ This time series has a skewed distribution of values.
```{r carpart-plot, dpi=300, out.width = "100%", fig.align='center', fig.cap="**Figure 1**: Carpart - monthly car part sales.", fig.dim = c(6, 3)}
layout(mat = matrix(c(1, 2), nrow = 1, ncol = 2), widths = c(2, 1))
-plot(carpart, xlab = "Time", ylab = "Car part sales", main = NULL)
-hist(carpart, xlab = "Car part sales", main = NULL)
+plot(carparts_example, xlab = "Time", ylab = "Car part sales", main = NULL)
+hist(carparts_example, xlab = "Car part sales", main = NULL)
```
We divide the time series into train and test, such that the test set
contains the last 12 months.
```{r train-test}
-train <- window(carpart, end = c(2001, 3))
-test <- window(carpart, start = c(2001, 4))
+train <- window(carparts_example, end = c(2001, 3))
+test <- window(carparts_example, start = c(2001, 4))
```
@@ -254,11 +244,11 @@ Note the improvements of the reconciled forecasts compared to the base forecasts
We now consider a *monthly* time series (N1485) from the M3 forecasting competition [@makridakis2000m3].
citerei il pacchetto mcomp da cui l'abbiamo presa
-It is available from `bayesRecon::M3sample`.
+It is available from `bayesRecon::M3_example`.
lo chiamerei m3_example
```{r m3-plot, dpi=300, out.width = "100%", fig.align='center', fig.cap="**Figure 3**: M3 - N1485 time series.", fig.dim = c(6, 3)}
-plot(M3example$train, xlab = "Time", ylab = "y", main = "N1485")
+plot(M3_example$train, xlab = "Time", ylab = "y", main = "N1485")
```
We build the temporal hierarchy using the `temporal_aggregation` function.
diff --git a/vignettes/references.bib b/vignettes/references.bib
index 283efd2..2cf4061 100644
--- a/vignettes/references.bib
+++ b/vignettes/references.bib
@@ -122,20 +122,23 @@ @InProceedings{corani2021probabilistic
booktitle="Machine Learning and Knowledge Discovery in Databases",
year="2021",
publisher="Springer International Publishing",
- address="Cham",
pages="211--226"
}
@article{zambon2022efficient,
title={Efficient probabilistic reconciliation of forecasts for real-valued and count time series},
author={Zambon, Lorenzo and Azzimonti, Dario and Corani, Giorgio},
- journal={stat},
- volume={1050},
- pages={8},
- year={2022}
+ journal={Statistics and Computing},
+ volume={34},
+ number={1},
+ pages={21},
+ year={2024},
+ publisher={Springer}
}
+
+
@article{zambon2023properties,
title={Properties of the reconciled distributions for Gaussian and count forecasts},
author={Zambon, Lorenzo and Agosto, Arianna and Giudici, Paolo and Corani, Giorgio},