Helper function for the tsraking() function that conveniently determines the required set of raking
problems to be solved and internally generates the individual calls to tsraking(). It is especially
useful in the context of temporal total (e.g., annual total) preservation where each individual raking
problem either involves a single period for incomplete temporal groups (e.g., incomplete years) or several
periods for complete temporal groups (e.g., the set of periods of a complete year).
Usage
tsraking_driver(
in_ts,
..., # `tsraking()` arguments excluding `data_df`
temporal_grp_periodicity = 1,
temporal_grp_start = 1
)Arguments
- in_ts
(mandatory)
Time series (object of class "ts" or "mts") that contains the time series data to be reconciled. They are the raking problems' input data (initial solutions).
- ...
Arguments passed on to
tsrakingmetadata_df(mandatory)
Data frame (object of class "data.frame") that describes the cross-sectional aggregation constraints (additivity rules) for the raking problem. Two character variables must be included in the metadata data frame:
seriesandtotal1. Two variables are optional:total2(character) andalterAnnual(numeric). The values of variableseriesrepresent the variable names of the component series in the input time series data frame (argumentdata_df). Similarly, the values of variablestotal1andtotal2represent the variable names of the 1st and 2nd dimension cross-sectional control totals in the input time series data frame. VariablealterAnnualcontains the alterability coefficient for the temporal constraint associated to each component series. When specified, the latter will override the default alterability coefficient specified with argumentalterAnnual.alterability_df(optional)
Data frame (object of class "data.frame"), or
NULL, that contains the alterability coefficients variables. They must correspond to a component series or a cross-sectional control total, that is, a variable with the same name must exist in the input time series data frame (argumentdata_df). The values of these alterability coefficients will override the default alterability coefficients specified with argumentsalterSeries,alterTotal1andalterTotal2. When the input time series data frame contains several observations and the alterability coefficients data frame contains only one, the alterability coefficients are used (repeated) for all observations of the input time series data frame. Alternatively, the alterability coefficients data frame may contain as many observations as the input time series data frame.Default value is
alterability_df = NULL(default alterability coefficients).alterSeries(optional)
Nonnegative real number specifying the default alterability coefficient for the component series values. It will apply to component series for which alterability coefficients have not already been specified in the alterability coefficients data frame (argument
alterability_df).Default value is
alterSeries = 1.0(nonbinding component series values).alterTotal1(optional)
Nonnegative real number specifying the default alterability coefficient for the 1st dimension cross-sectional control totals. It will apply to cross-sectional control totals for which alterability coefficients have not already been specified in the alterability coefficients data frame (argument
alterability_df).Default value is
alterTotal1 = 0.0(binding 1st dimension cross-sectional control totals)alterTotal2(optional)
Nonnegative real number specifying the default alterability coefficient for the 2nd dimension cross-sectional control totals. It will apply to cross-sectional control totals for which alterability coefficients have not already been specified in the alterability coefficients data frame (argument
alterability_df).Default value is
alterTotal2 = 0.0(binding 2nd dimension cross-sectional control totals).alterAnnual(optional)
Nonnegative real number specifying the default alterability coefficient for the component series temporal constraints (e.g., annual totals). It will apply to component series for which alterability coefficients have not already been specified in the metadata data frame (argument
metadata_df).Default value is
alterAnnual = 0.0(binding temporal control totals).tolV,tolP(optional)
Nonnegative real number, or
NA, specifying the tolerance, in absolute value or percentage, to be used when performing the ultimate test in the case of binding totals (alterability coefficient of \(0.0\) for temporal or cross-sectional control totals). The test compares the input binding control totals with the ones calculated from the reconciled (output) component series. ArgumentstolVandtolPcannot be both specified together (one must be specified while the other must beNA).Example: to set a tolerance of 10 units, specify
tolV = 10, tolP = NA; to set a tolerance of 1%, specifytolV = NA, tolP = 0.01.Default values are
tolV = 0.001andtolP = NA.warnNegResult(optional)
Logical argument specifying whether a warning message is generated when a negative value created by the function in the reconciled (output) series is smaller than the threshold specified by argument
tolN.Default value is
warnNegResult = TRUE.tolN(optional)
Negative real number specifying the threshold for the identification of negative values. A value is considered negative when it is smaller than this threshold.
Default value is
tolN = -0.001.id(optional)
String vector (minimum length of 1), or
NULL, specifying the name of additional variables to be transferred from the input time series data frame (argumentdata_df) to the output time series data frame, the object returned by the function (see section Value). By default, the output series data frame only contains the variables listed in the metadata data frame (argumentmetadata_df).Default value is
id = NULL.verbose(optional)
Logical argument specifying whether information on intermediate steps with execution time (real time, not CPU time) should be displayed. Note that specifying argument
quiet = TRUEwould nullify argumentverbose.Default value is
verbose = FALSE.Vmat_option(optional)
Specification of the option for the variance matrices (\(V_e\) and \(V_\epsilon\); see section Details):
Value Description 1Use vectors \(x\) and \(g\) in the variance matrices. 2Use vectors \(|x|\) and \(|g|\) in the variance matrices. See Ferland (2016) and subsection Arguments
Vmat_optionandwarnNegInputin section Details for more information.Default value is
Vmat_option = 1.warnNegInput(optional)
Logical argument specifying whether a warning message is generated when a negative value smaller than the threshold specified by argument
tolNis found in the input time series data frame (argumentdata_df).Default value is
warnNegInput = TRUE.quiet(optional)
Logical argument specifying whether or not to display only essential information such as warnings and errors. Specifying
quiet = TRUEwould also nullify argumentverboseand is equivalent to wrapping yourtsraking()call withsuppressMessages().Default value is
quiet = FALSE.
- temporal_grp_periodicity
(optional)
Positive integer defining the number of periods in temporal groups for which the totals should be preserved. E.g., specify
temporal_grp_periodicity = 3with a monthly time series for quarterly total preservation andtemporal_grp_periodicity = 12(ortemporal_grp_periodicity = frequency(in_ts)) for annual total preservation. Specifyingtemporal_grp_periodicity = 1(default) corresponds to period-by-period processing without temporal total preservation.Default value is
temporal_grp_periodicity = 1(period-by-period processing without temporal total preservation).- temporal_grp_start
(optional)
Integer in the [1 ..
temporal_grp_periodicity] interval specifying the starting period (cycle) for temporal total preservation. E.g., annual totals corresponding to fiscal years defined from April to March of the following year would be specified withtemporal_grp_start = 4for a monthly time series (frequency(in_ts) = 12) andtemporal_grp_start = 2for a quarterly time series (frequency(in_ts) = 4). This argument has no effect for period-by-period processing without temporal total preservation (temporal_grp_periodicity = 1).Default value is
temporal_grp_start = 1.
Value
The function returns a time series object (class "ts" or "mts") containing the reconciled component series,
reconciled cross-sectional control totals and other series specified with tsraking() argument id. It can be
explicitly coerced to another type of object with the appropriate as*() function (e.g., tsibble::as_tsibble()
would coerce it to a tsibble).
Note that a NULL object is returned if an error occurs before data processing could start. Otherwise, if execution
gets far enough so that data processing could start, then an incomplete object (with NA values) would be returned
in case of errors.
Details
This function solves one raking problem with tsraking() per processing group (see section Processing groups for
details). The mathematical expression of these raking problem can be found in the Details section of the tsraking()
documentation.
The alterability coefficients data frame (argument alterability_df) specified with tsraking_driver() can either
contain:
A single observation: the specified coefficients will be used for all periods of input time series object (argument
in_ts).A number of observations equal to
frequency(in_ts): the specified coefficients will be used for the corresponding cycle of the input time series object (argumentin_ts) periods. Monthly data example: 1st observation for January, 2nd observation for February, etc.).A number of observations equal to
nrow(in_ts): the specified coefficients will be used for the corresponding periods of the input time series object (argumentin_ts), i.e., 1st observation for the 1st period, 2nd observation for the 2nd period, etc.).
Specifying quiet = TRUE will suppress the tsraking() messages (e.g., function header) and only
display essential information such as warnings, errors and the period (or set of periods) being reconciled.
We advise against wrapping your tsraking_driver() function call with suppressMessages() to further
suppress the display of the raking period(s) information as this would make troubleshooting difficult
in case of issues with individual raking problems.
Although tsraking() could be called with *apply() to successively reconcile all the periods of the input time
series (in_ts), using tsraking_driver() has a few advantages, namely:
temporal total preservation (only period-by-period processing, without temporal total preservation, would be possible with
*apply());more flexibility in the specification of user-defined alterability coefficients (e.g., period-specific values);
display of the period being processed (reconciled) in the console, which is useful for troubleshooting individual raking problems;
improved error handling, i.e., better management of warnings or errors if they were to occur only for some raking problems (periods);
readily returns a "ts" ("mts") object.
Processing groups
The set of periods of a given reconciliation (raking or balancing) problem is called a processing group and either corresponds to:
a single period with period-by-period processing or, when preserving temporal totals, for the individual periods of an incomplete temporal group (e.g., an incomplete year)
or the set of periods of a complete temporal group (e.g., a complete year) when preserving temporal totals.
The total number of processing groups (total number of reconciliation problems) depends on the set of
periods in the input time series object (argument in_ts) and on the value of arguments
temporal_grp_periodicity and temporal_grp_start.
Common scenarios include temporal_grp_periodicity = 1 (default) for period-by period processing without
temporal total preservation and temporal_grp_periodicity = frequency(in_ts) for the preservation of annual
totals (calendar years by default). Argument temporal_grp_start allows the specification of other types of
(non-calendar) years. E.g., fiscal years starting on April correspond to temporal_grp_start = 4 with monthly
data and temporal_grp_start = 2 with quarterly data. Preserving quarterly totals with monthly data would
correspond to temporal_grp_periodicity = 3.
By default, temporal groups covering more than a year (i.e., corresponding to temporal_grp_periodicity > frequency(in_ts) start on a
year that is a multiple of
ceiling(temporal_grp_periodicity / frequency(in_ts)). E.g., biennial groups corresponding to temporal_grp_periodicity = 2 * frequency(in_ts)
start on an even year by default. This behaviour can be changed with argument temporal_grp_start. E.g., the
preservation of biennial totals starting on an odd year instead of an even year (default) corresponds to
temporal_grp_start = frequency(in_ts) + 1 (along with temporal_grp_periodicity = 2 * frequency(in_ts)).
See the gs.build_proc_grps() Examples for common processing group scenarios.
References
Statistics Canada (2018). "Chapter 6: Advanced topics", Theory and Application of Reconciliation (Course code 0437), Statistics Canada, Ottawa, Canada.
Examples
# 1-dimensional raking problem where the quarterly sales of cars in the 3 prairie
# provinces (Alb., Sask. and Man.) for 8 quarters, from 2019 Q2 to 2021 Q1, must
# sum up to the total (`cars_tot`).
# Problem metadata
my_metadata <- data.frame(series = c("cars_alb", "cars_sask", "cars_man"),
total1 = rep("cars_tot", 3))
my_metadata
#> series total1
#> 1 cars_alb cars_tot
#> 2 cars_sask cars_tot
#> 3 cars_man cars_tot
# Problem data
my_series <- ts(matrix(c(14, 18, 14, 58,
17, 14, 16, 44,
14, 19, 18, 58,
20, 18, 12, 53,
16, 16, 19, 44,
14, 15, 16, 50,
19, 20, 14, 52,
16, 15, 19, 51),
ncol = 4,
byrow = TRUE,
dimnames = list(NULL, c("cars_alb", "cars_sask",
"cars_man", "cars_tot"))),
start = c(2019, 2),
frequency = 4)
###########
# Example 1: Period-by-period processing without temporal total preservation.
# Reconcile the data
out_raked1 <- tsraking_driver(my_series, my_metadata)
#>
#>
#> Raking period [2019-2]
#> ======================
#>
#>
#> --- Package gseries 3.0.2 - Improve the Coherence of Your Time Series Data ---
#> Created on June 16, 2025, at 3:11:30 PM EDT
#> URL: https://StatCan.github.io/gensol-gseries/en/
#> https://StatCan.github.io/gensol-gseries/fr/
#> Email: g-series@statcan.gc.ca
#>
#> tsraking() function:
#> data_df = <argument 'data_df'>
#> metadata_df = <argument 'metadata_df'>
#> alterability_df = NULL (default)
#> alterSeries = 1 (default)
#> alterTotal1 = 0 (default)
#> alterTotal2 = 0 (default)
#> alterAnnual = 0 (default)
#> tolV = 0.001 (default)
#> warnNegResult = TRUE (default)
#> tolN = -0.001 (default)
#> id = NULL (default)
#> verbose = FALSE (default)
#> (*)Vmat_option = 1 (default)
#> (*)warnNegInput = TRUE (default)
#> (*)quiet = FALSE (default)
#> (*) indicates new arguments in G-Series 3.0
#>
#>
#>
#> Raking period [2019-3]
#> ======================
#>
#>
#> --- Package gseries 3.0.2 - Improve the Coherence of Your Time Series Data ---
#> Created on June 16, 2025, at 3:11:30 PM EDT
#> URL: https://StatCan.github.io/gensol-gseries/en/
#> https://StatCan.github.io/gensol-gseries/fr/
#> Email: g-series@statcan.gc.ca
#>
#> tsraking() function:
#> data_df = <argument 'data_df'>
#> metadata_df = <argument 'metadata_df'>
#> alterability_df = NULL (default)
#> alterSeries = 1 (default)
#> alterTotal1 = 0 (default)
#> alterTotal2 = 0 (default)
#> alterAnnual = 0 (default)
#> tolV = 0.001 (default)
#> warnNegResult = TRUE (default)
#> tolN = -0.001 (default)
#> id = NULL (default)
#> verbose = FALSE (default)
#> (*)Vmat_option = 1 (default)
#> (*)warnNegInput = TRUE (default)
#> (*)quiet = FALSE (default)
#> (*) indicates new arguments in G-Series 3.0
#>
#>
#>
#> Raking period [2019-4]
#> ======================
#>
#>
#> --- Package gseries 3.0.2 - Improve the Coherence of Your Time Series Data ---
#> Created on June 16, 2025, at 3:11:30 PM EDT
#> URL: https://StatCan.github.io/gensol-gseries/en/
#> https://StatCan.github.io/gensol-gseries/fr/
#> Email: g-series@statcan.gc.ca
#>
#> tsraking() function:
#> data_df = <argument 'data_df'>
#> metadata_df = <argument 'metadata_df'>
#> alterability_df = NULL (default)
#> alterSeries = 1 (default)
#> alterTotal1 = 0 (default)
#> alterTotal2 = 0 (default)
#> alterAnnual = 0 (default)
#> tolV = 0.001 (default)
#> warnNegResult = TRUE (default)
#> tolN = -0.001 (default)
#> id = NULL (default)
#> verbose = FALSE (default)
#> (*)Vmat_option = 1 (default)
#> (*)warnNegInput = TRUE (default)
#> (*)quiet = FALSE (default)
#> (*) indicates new arguments in G-Series 3.0
#>
#>
#>
#> Raking period [2020-1]
#> ======================
#>
#>
#> --- Package gseries 3.0.2 - Improve the Coherence of Your Time Series Data ---
#> Created on June 16, 2025, at 3:11:30 PM EDT
#> URL: https://StatCan.github.io/gensol-gseries/en/
#> https://StatCan.github.io/gensol-gseries/fr/
#> Email: g-series@statcan.gc.ca
#>
#> tsraking() function:
#> data_df = <argument 'data_df'>
#> metadata_df = <argument 'metadata_df'>
#> alterability_df = NULL (default)
#> alterSeries = 1 (default)
#> alterTotal1 = 0 (default)
#> alterTotal2 = 0 (default)
#> alterAnnual = 0 (default)
#> tolV = 0.001 (default)
#> warnNegResult = TRUE (default)
#> tolN = -0.001 (default)
#> id = NULL (default)
#> verbose = FALSE (default)
#> (*)Vmat_option = 1 (default)
#> (*)warnNegInput = TRUE (default)
#> (*)quiet = FALSE (default)
#> (*) indicates new arguments in G-Series 3.0
#>
#>
#>
#> Raking period [2020-2]
#> ======================
#>
#>
#> --- Package gseries 3.0.2 - Improve the Coherence of Your Time Series Data ---
#> Created on June 16, 2025, at 3:11:30 PM EDT
#> URL: https://StatCan.github.io/gensol-gseries/en/
#> https://StatCan.github.io/gensol-gseries/fr/
#> Email: g-series@statcan.gc.ca
#>
#> tsraking() function:
#> data_df = <argument 'data_df'>
#> metadata_df = <argument 'metadata_df'>
#> alterability_df = NULL (default)
#> alterSeries = 1 (default)
#> alterTotal1 = 0 (default)
#> alterTotal2 = 0 (default)
#> alterAnnual = 0 (default)
#> tolV = 0.001 (default)
#> warnNegResult = TRUE (default)
#> tolN = -0.001 (default)
#> id = NULL (default)
#> verbose = FALSE (default)
#> (*)Vmat_option = 1 (default)
#> (*)warnNegInput = TRUE (default)
#> (*)quiet = FALSE (default)
#> (*) indicates new arguments in G-Series 3.0
#>
#>
#>
#> Raking period [2020-3]
#> ======================
#>
#>
#> --- Package gseries 3.0.2 - Improve the Coherence of Your Time Series Data ---
#> Created on June 16, 2025, at 3:11:30 PM EDT
#> URL: https://StatCan.github.io/gensol-gseries/en/
#> https://StatCan.github.io/gensol-gseries/fr/
#> Email: g-series@statcan.gc.ca
#>
#> tsraking() function:
#> data_df = <argument 'data_df'>
#> metadata_df = <argument 'metadata_df'>
#> alterability_df = NULL (default)
#> alterSeries = 1 (default)
#> alterTotal1 = 0 (default)
#> alterTotal2 = 0 (default)
#> alterAnnual = 0 (default)
#> tolV = 0.001 (default)
#> warnNegResult = TRUE (default)
#> tolN = -0.001 (default)
#> id = NULL (default)
#> verbose = FALSE (default)
#> (*)Vmat_option = 1 (default)
#> (*)warnNegInput = TRUE (default)
#> (*)quiet = FALSE (default)
#> (*) indicates new arguments in G-Series 3.0
#>
#>
#>
#> Raking period [2020-4]
#> ======================
#>
#>
#> --- Package gseries 3.0.2 - Improve the Coherence of Your Time Series Data ---
#> Created on June 16, 2025, at 3:11:30 PM EDT
#> URL: https://StatCan.github.io/gensol-gseries/en/
#> https://StatCan.github.io/gensol-gseries/fr/
#> Email: g-series@statcan.gc.ca
#>
#> tsraking() function:
#> data_df = <argument 'data_df'>
#> metadata_df = <argument 'metadata_df'>
#> alterability_df = NULL (default)
#> alterSeries = 1 (default)
#> alterTotal1 = 0 (default)
#> alterTotal2 = 0 (default)
#> alterAnnual = 0 (default)
#> tolV = 0.001 (default)
#> warnNegResult = TRUE (default)
#> tolN = -0.001 (default)
#> id = NULL (default)
#> verbose = FALSE (default)
#> (*)Vmat_option = 1 (default)
#> (*)warnNegInput = TRUE (default)
#> (*)quiet = FALSE (default)
#> (*) indicates new arguments in G-Series 3.0
#>
#>
#>
#> Raking period [2021-1]
#> ======================
#>
#>
#> --- Package gseries 3.0.2 - Improve the Coherence of Your Time Series Data ---
#> Created on June 16, 2025, at 3:11:30 PM EDT
#> URL: https://StatCan.github.io/gensol-gseries/en/
#> https://StatCan.github.io/gensol-gseries/fr/
#> Email: g-series@statcan.gc.ca
#>
#> tsraking() function:
#> data_df = <argument 'data_df'>
#> metadata_df = <argument 'metadata_df'>
#> alterability_df = NULL (default)
#> alterSeries = 1 (default)
#> alterTotal1 = 0 (default)
#> alterTotal2 = 0 (default)
#> alterAnnual = 0 (default)
#> tolV = 0.001 (default)
#> warnNegResult = TRUE (default)
#> tolN = -0.001 (default)
#> id = NULL (default)
#> verbose = FALSE (default)
#> (*)Vmat_option = 1 (default)
#> (*)warnNegInput = TRUE (default)
#> (*)quiet = FALSE (default)
#> (*) indicates new arguments in G-Series 3.0
#>
# Initial data
my_series
#> cars_alb cars_sask cars_man cars_tot
#> 2019 Q2 14 18 14 58
#> 2019 Q3 17 14 16 44
#> 2019 Q4 14 19 18 58
#> 2020 Q1 20 18 12 53
#> 2020 Q2 16 16 19 44
#> 2020 Q3 14 15 16 50
#> 2020 Q4 19 20 14 52
#> 2021 Q1 16 15 19 51
# Reconciled data
out_raked1
#> cars_alb cars_sask cars_man cars_tot
#> 2019 Q2 17.65217 22.69565 17.65217 58
#> 2019 Q3 15.91489 13.10638 14.97872 44
#> 2019 Q4 15.92157 21.60784 20.47059 58
#> 2020 Q1 21.20000 19.08000 12.72000 53
#> 2020 Q2 13.80392 13.80392 16.39216 44
#> 2020 Q3 15.55556 16.66667 17.77778 50
#> 2020 Q4 18.64151 19.62264 13.73585 52
#> 2021 Q1 16.32000 15.30000 19.38000 51
# Check the output cross-sectional constraint
all.equal(rowSums(out_raked1[, my_metadata$series]), as.vector(out_raked1[, "cars_tot"]))
#> [1] TRUE
# Check the control total (fixed)
all.equal(my_series[, "cars_tot"], out_raked1[, "cars_tot"])
#> [1] TRUE
###########
# Example 2: Annual total preservation for year 2020 (period-by-period processing
# for incomplete years 2019 and 2021), with `quiet = TRUE` to avoid
# displaying the function header for all processing groups.
# First, check that the 2020 annual total for the total series (`cars_tot`) and the
# sum of the component series (`cars_alb`, `cars_sask` and `cars_man`) matches.
# Otherwise, this "grand total" discrepancy would first have to be resolved before
# calling `tsraking_driver()`.
tot2020 <- aggregate.ts(window(my_series, start = c(2020, 1), end = c(2020, 4)))
all.equal(as.numeric(tot2020[, "cars_tot"]), sum(tot2020[, my_metadata$series]))
#> [1] TRUE
# Reconcile the data
out_raked2 <- tsraking_driver(in_ts = my_series,
metadata_df = my_metadata,
quiet = TRUE,
temporal_grp_periodicity = frequency(my_series))
#>
#>
#> Raking period [2019-2]
#> ======================
#>
#>
#> Raking period [2019-3]
#> ======================
#>
#>
#> Raking period [2019-4]
#> ======================
#>
#>
#> Raking periods [2020-1 - 2020-4]
#> ================================
#>
#>
#> Raking period [2021-1]
#> ======================
# Initial data
my_series
#> cars_alb cars_sask cars_man cars_tot
#> 2019 Q2 14 18 14 58
#> 2019 Q3 17 14 16 44
#> 2019 Q4 14 19 18 58
#> 2020 Q1 20 18 12 53
#> 2020 Q2 16 16 19 44
#> 2020 Q3 14 15 16 50
#> 2020 Q4 19 20 14 52
#> 2021 Q1 16 15 19 51
# Reconciled data
out_raked2
#> cars_alb cars_sask cars_man cars_tot
#> 2019 Q2 17.65217 22.69565 17.65217 58
#> 2019 Q3 15.91489 13.10638 14.97872 44
#> 2019 Q4 15.92157 21.60784 20.47059 58
#> 2020 Q1 21.15283 19.04513 12.80204 53
#> 2020 Q2 13.74700 13.75373 16.49927 44
#> 2020 Q3 15.50782 16.62184 17.87034 50
#> 2020 Q4 18.59234 19.57931 13.82835 52
#> 2021 Q1 16.32000 15.30000 19.38000 51
# Check the output cross-sectional constraint
all.equal(rowSums(out_raked2[, my_metadata$series]), as.vector(out_raked2[, "cars_tot"]))
#> [1] TRUE
# Check the output temporal constraints (2020 annual totals for each series)
all.equal(tot2020,
aggregate.ts(window(out_raked2, start = c(2020, 1), end = c(2020, 4))))
#> [1] TRUE
# Check the control total (fixed)
all.equal(my_series[, "cars_tot"], out_raked2[, "cars_tot"])
#> [1] TRUE
###########
# Example 3: Annual total preservation for fiscal years defined from April to March
# (2019Q2-2020Q1 and 2020Q2-2021Q1).
# Calculate the fiscal year totals (as an annual "ts" object)
fiscalYr_tot <- ts(rbind(aggregate.ts(window(my_series,
start = c(2019, 2),
end = c(2020, 1))),
aggregate.ts(window(my_series,
start = c(2020, 2),
end = c(2021, 1)))),
start = 2019,
frequency = 1)
# Discrepancies in both fiscal year totals (total series vs. sum of the component series)
as.numeric(fiscalYr_tot[, "cars_tot"]) - rowSums(fiscalYr_tot[, my_metadata$series])
#> [1] 19 -2
# 3a) Reconcile the fiscal year totals (rake the fiscal year totals of the component series
# to those of the total series).
new_fiscalYr_tot <- tsraking_driver(in_ts = fiscalYr_tot,
metadata_df = my_metadata,
quiet = TRUE)
#>
#>
#> Raking period [2019]
#> ====================
#>
#>
#> Raking period [2020]
#> ====================
# Confirm that the previous discrepancies are now "gone" (are both zero)
as.numeric(new_fiscalYr_tot[, "cars_tot"]) - rowSums(new_fiscalYr_tot[, my_metadata$series])
#> [1] 0 0
# 3b) Benchmark the quarterly component series to these new (coherent) fiscal year totals.
out_bench <- benchmarking(series_df = ts_to_tsDF(my_series[, my_metadata$series]),
benchmarks_df = ts_to_bmkDF(
new_fiscalYr_tot[, my_metadata$series],
ind_frequency = frequency(my_series),
# Fiscal years starting on Q2 (April)
bmk_interval_start = 2),
rho = 0.729,
lambda = 1,
biasOption = 2,
allCols = TRUE,
quiet = TRUE)
#>
#> Benchmarking indicator series [cars_alb] with benchmarks [cars_alb]
#> -------------------------------------------------------------------
#>
#> Benchmarking indicator series [cars_sask] with benchmarks [cars_sask]
#> ---------------------------------------------------------------------
#>
#> Benchmarking indicator series [cars_man] with benchmarks [cars_man]
#> -------------------------------------------------------------------
my_new_ser <- tsDF_to_ts(cbind(out_bench$series, cars_tot = my_series[, "cars_tot"]),
frequency = frequency(my_series))
# 3c) Reconcile the quarterly data with preservation of fiscal year totals.
out_raked3 <- tsraking_driver(in_ts = my_new_ser,
metadata_df = my_metadata,
temporal_grp_periodicity = frequency(my_series),
# Fiscal years starting on Q2 (April)
temporal_grp_start = 2,
quiet = TRUE)
#>
#>
#> Raking periods [2019-2 - 2020-1]
#> ================================
#>
#>
#> Raking periods [2020-2 - 2021-1]
#> ================================
# Initial data
my_series
#> cars_alb cars_sask cars_man cars_tot
#> 2019 Q2 14 18 14 58
#> 2019 Q3 17 14 16 44
#> 2019 Q4 14 19 18 58
#> 2020 Q1 20 18 12 53
#> 2020 Q2 16 16 19 44
#> 2020 Q3 14 15 16 50
#> 2020 Q4 19 20 14 52
#> 2021 Q1 16 15 19 51
# With coherent fiscal year totals
my_new_ser
#> cars_alb cars_sask cars_man cars_tot
#> 2019 Q2 15.40737 19.84939 15.41097 58
#> 2019 Q3 18.90614 15.54094 17.78267 44
#> 2019 Q4 15.45221 20.98489 19.84697 58
#> 2020 Q1 21.60026 19.38252 12.83568 53
#> 2020 Q2 16.44068 16.41619 19.39307 44
#> 2020 Q3 13.91243 14.89512 15.85700 50
#> 2020 Q4 18.49133 19.47043 13.65082 52
#> 2021 Q1 15.50230 14.55494 18.41569 51
# Reconciled data
out_raked3
#> cars_alb cars_sask cars_man cars_tot
#> 2019 Q2 17.77916 22.53212 17.68872 58
#> 2019 Q3 16.07232 12.91959 15.00808 44
#> 2019 Q4 16.06497 21.42285 20.51217 58
#> 2020 Q1 21.44952 18.88317 12.66732 53
#> 2020 Q2 13.84015 13.79962 16.36024 44
#> 2020 Q3 15.57114 16.65292 17.77593 50
#> 2020 Q4 18.62985 19.59265 13.77750 52
#> 2021 Q1 16.30560 15.29149 19.40291 51
# Check the output cross-sectional constraint
all.equal(rowSums(out_raked3[, my_metadata$series]), as.vector(out_raked3[, "cars_tot"]))
#> [1] TRUE
# Check the output temporal constraints (both fiscal year totals for all series)
all.equal(rbind(aggregate.ts(window(my_new_ser, start = c(2019, 2), end = c(2020, 1))),
aggregate.ts(window(my_new_ser, start = c(2020, 2), end = c(2021, 1)))),
rbind(aggregate.ts(window(out_raked3, start = c(2019, 2), end = c(2020, 1))),
aggregate.ts(window(out_raked3, start = c(2020, 2), end = c(2021, 1)))))
#> [1] TRUE
# Check the control total (fixed)
all.equal(my_series[, "cars_tot"], out_raked3[, "cars_tot"])
#> [1] TRUE