Convert a (non-stacked) time series data frame (benchmarking() and stock_benchmarking() data format)
into a "ts" (or "mts") object.
This function is useful to convert the benchmarked data frame returned by a call to benchmarking() or
stock_benchmarking() into a "ts" object, where one or several series were benchmarked in non BY-group
processing mode. Stacked time series data frames associated to executions in BY-group mode must first be
unstacked with unstack_tsDF().
Arguments
- ts_df
(mandatory)
Data frame (object of class "data.frame") to be converted.
- frequency
(mandatory)
Integer specifying the frequency of the time series to be converted. The frequency of a time series corresponds to the maximum number of periods in a year (12 for a monthly data, 4 for a quarterly data, 1 for annual data).
- yr_cName, per_cName
(optional)
Strings specifying the name of the numeric variables (columns) in the input data frame that contain the data point year and period identifiers.
Default values are
yr_cName = "year"andper_cName = "period".
Value
The function returns a time series object (class "ts" or "mts"), which 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).
Examples
# Initial quarterly time series (indicator series to be benchmarked)
qtr_ts <- ts(c(1.9, 2.4, 3.1, 2.2, 2.0, 2.6, 3.4, 2.4, 2.3),
start = c(2015, 1), frequency = 4)
# Annual time series (benchmarks)
ann_ts <- ts(c(10.3, 10.2), start = 2015, frequency = 1)
# Proportional benchmarking
out_bench <- benchmarking(ts_to_tsDF(qtr_ts),
ts_to_bmkDF(ann_ts, ind_frequency = 4),
rho = 0.729, lambda = 1, biasOption = 3,
quiet = TRUE)
# Initial and final (benchmarked) quarterly time series ("ts" objects)
qtr_ts
#> Qtr1 Qtr2 Qtr3 Qtr4
#> 2015 1.9 2.4 3.1 2.2
#> 2016 2.0 2.6 3.4 2.4
#> 2017 2.3
tsDF_to_ts(out_bench$series, frequency = 4)
#> Qtr1 Qtr2 Qtr3 Qtr4
#> 2015 2.049326 2.601344 3.337638 2.311691
#> 2016 2.021090 2.554801 3.292193 2.331915
#> 2017 2.268017
# Proportional end-of-year stock benchmarking - multiple (3) series processed
# with argument `by` (in BY-group mode)
qtr_mts <- ts.union(ser1 = qtr_ts, ser2 = qtr_ts * 100, ser3 = qtr_ts * 10)
ann_mts <- ts.union(ser1 = ann_ts / 4, ser2 = ann_ts * 25, ser3 = ann_ts * 2.5)
out_bench2 <- stock_benchmarking(stack_tsDF(ts_to_tsDF(qtr_mts)),
stack_bmkDF(ts_to_bmkDF(
ann_mts, ind_frequency = 4,
discrete_flag = TRUE, alignment = "e")),
rho = 0.729, lambda = 1, biasOption = 3,
by = "series",
quiet = TRUE)
#>
#> Benchmarking by-group 1 (series=ser1)
#> =====================================
#>
#> Benchmarking by-group 2 (series=ser2)
#> =====================================
#>
#> Benchmarking by-group 3 (series=ser3)
#> =====================================
# Initial and final (benchmarked) quarterly time series ("mts" objects)
qtr_mts
#> ser1 ser2 ser3
#> 2015 Q1 1.9 190 19
#> 2015 Q2 2.4 240 24
#> 2015 Q3 3.1 310 31
#> 2015 Q4 2.2 220 22
#> 2016 Q1 2.0 200 20
#> 2016 Q2 2.6 260 26
#> 2016 Q3 3.4 340 34
#> 2016 Q4 2.4 240 24
#> 2017 Q1 2.3 230 23
tsDF_to_ts(unstack_tsDF(out_bench2$series), frequency = 4)
#> ser1 ser2 ser3
#> 2015 Q1 2.172021 217.2021 21.72021
#> 2015 Q2 2.784446 278.4446 27.84446
#> 2015 Q3 3.633922 363.3922 36.33922
#> 2015 Q4 2.575000 257.5000 25.75000
#> 2016 Q1 2.298694 229.8694 22.98694
#> 2016 Q2 2.903718 290.3718 29.03718
#> 2016 Q3 3.685986 368.5986 36.85986
#> 2016 Q4 2.550000 255.0000 25.50000
#> 2017 Q1 2.437793 243.7793 24.37793