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Time series aggregation involves grouping observations and summarizing group values with a statistical function. This operation is useful to:

  • Decrease (downsampling) the temporal resolution of a time series.

  • Highlight particular states of a time series over time. For example, a daily temperature series can be aggregated by month using max to represent the highest temperatures each month.

  • Transform irregular time series into regular.

This function aggregates time series lists with overlapping times. Please check such overlap by assessing the columns "begin" and "end " of the data frame resulting from df <- tsl_time(tsl = tsl). Aggregation will be limited by the shortest time series in your time series list. To aggregate non-overlapping time series, please subset the individual components of tsl one by one either using tsl_subset() or the syntax tsl = my_tsl[[i]].

Methods

Any function returning a single number from a numeric vector can be used to aggregate a time series list. Quoted and unquoted function names can be used. Additional arguments to these functions can be passed via the argument .... Typical examples are:

This function supports a parallelization setup via future::plan(), and progress bars provided by the package progressr.

Usage

tsl_aggregate(tsl = NULL, new_time = NULL, f = mean, ...)

Arguments

tsl

(required, list) Time series list. Default: NULL

new_time

(required, numeric, numeric vector, Date vector, POSIXct vector, or keyword) Definition of the aggregation pattern. The available options are:

  • numeric vector: only for the "numeric" time class, defines the breakpoints for time series aggregation.

  • "Date" or "POSIXct" vector: as above, but for the time classes "Date" and "POSIXct." In any case, the input vector is coerced to the time class of the tsl argument.

  • numeric: defines fixed time intervals in the units of tsl for time series aggregation. Used as is when the time class is "numeric", and coerced to integer and interpreted as days for the time classes "Date" and "POSIXct".

  • keyword (see utils_time_units()): the common options for the time classes "Date" and "POSIXct" are: "millennia", "centuries", "decades", "years", "quarters", "months", and "weeks". Exclusive keywords for the "POSIXct" time class are: "days", "hours", "minutes", and "seconds". The time class "numeric" accepts keywords coded as scientific numbers, from "1e8" to "1e-8".

f

(required, function name) Name of function taking a vector as input and returning a single value as output. Typical examples are mean, max,min, median, and quantile. Default: mean.

...

(optional) further arguments for f.

Value

time series list

See also

Examples


# yearly aggregation
#----------------------------------
#long-term monthly temperature of 20 cities
tsl <- tsl_initialize(
  x = cities_temperature,
  name_column = "name",
  time_column = "time"
)

#plot time series
if(interactive()){
  tsl_plot(
    tsl = tsl[1:4],
    guide_columns = 4
  )
}

#check time features
tsl_time(tsl)[, c("name", "resolution", "units")]
#>                name resolution units
#> 1           Bangkok    30.4381  days
#> 2            Bogotá    30.4381  days
#> 3             Cairo    30.4381  days
#> 4             Dhaka    30.4381  days
#> 5  Ho Chi Minh City    30.4381  days
#> 6          Istanbul    30.4381  days
#> 7           Jakarta    30.4381  days
#> 8           Karachi    30.4381  days
#> 9          Kinshasa    30.4381  days
#> 10            Lagos    30.4381  days
#> 11             Lima    30.4381  days
#> 12           London    30.4381  days
#> 13      Los Angeles    30.4381  days
#> 14           Manila    30.4381  days
#> 15           Moscow    30.4381  days
#> 16            Paris    30.4381  days
#> 17   Rio De Janeiro    30.4381  days
#> 18         Shanghai    30.4381  days
#> 19        São Paulo    30.4381  days
#> 20            Tokyo    30.4381  days

#aggregation: mean yearly values
tsl_year <- tsl_aggregate(
  tsl = tsl,
  new_time = "year",
  f = mean
)

#' #check time features
tsl_time(tsl_year)[, c("name", "resolution", "units")]
#>                name resolution units
#> 1           Bangkok     365.25  days
#> 2            Bogotá     365.25  days
#> 3             Cairo     365.25  days
#> 4             Dhaka     365.25  days
#> 5  Ho Chi Minh City     365.25  days
#> 6          Istanbul     365.25  days
#> 7           Jakarta     365.25  days
#> 8           Karachi     365.25  days
#> 9          Kinshasa     365.25  days
#> 10            Lagos     365.25  days
#> 11             Lima     365.25  days
#> 12           London     365.25  days
#> 13      Los Angeles     365.25  days
#> 14           Manila     365.25  days
#> 15           Moscow     365.25  days
#> 16            Paris     365.25  days
#> 17   Rio De Janeiro     365.25  days
#> 18         Shanghai     365.25  days
#> 19        São Paulo     365.25  days
#> 20            Tokyo     365.25  days

if(interactive()){
  tsl_plot(
    tsl = tsl_year[1:4],
    guide_columns = 4
  )
}


# other supported keywords
#----------------------------------

#simulate full range of calendar dates
tsl <- tsl_simulate(
  n = 2,
  rows = 1000,
  time_range = c(
    "0000-01-01",
    as.character(Sys.Date())
  )
)

#mean value by millennia (extreme case!!!)
tsl_millennia <- tsl_aggregate(
  tsl = tsl,
  new_time = "millennia",
  f = mean
)

if(interactive()){
  tsl_plot(tsl_millennia)
}

#max value by centuries
tsl_century <- tsl_aggregate(
  tsl = tsl,
  new_time = "century",
  f = max
)

if(interactive()){
  tsl_plot(tsl_century)
}

#quantile 0.75 value by centuries
tsl_centuries <- tsl_aggregate(
  tsl = tsl,
  new_time = "centuries",
  f = stats::quantile,
  probs = 0.75 #argument of stats::quantile()
)