aggregate_xts: Aggregate values in xts objects

In SEI: Calculating Standardised Indices

Aggregate values in xts objects

Description

Inputs an xts time series and outputs an xts time series whose values have been aggregated over a moving window of a user-specified length.

Usage

aggregate_xts(
  x,
  agg_period = 1,
  agg_scale = c("days", "mins", "hours", "weeks", "months", "years"),
  agg_fun = "sum",
  timescale = c("days", "mins", "hours", "weeks", "months", "years"),
  na_thres = 10
)

Arguments

x	xts object to be aggregated.
agg_period	length of the aggregation period.
agg_scale	timescale of agg_period; one of 'mins', 'hours', 'days', 'weeks', 'months', 'years'.
agg_fun	string specifying the function used to aggregate the data over the aggregation period, default is 'sum'.
timescale	timescale of the data; one of 'mins', 'hours', 'days', 'weeks', 'months', 'years'.
na_thres	threshold for the percentage of NA values allowed in the aggregation period; default is 10%.

Details

This has been adapted from code available at https://github.com/WillemMaetens/standaRdized.

Given a vector x_{1}, x_{2}, \dots, the function aggregate_xts calculates aggregated values \tilde{x}_{1}, \tilde{x}_{2}, \dots as

\tilde{x}_{t} = f(x_{t}, x_{t-1}, \dots, x_{t - k + 1}),

for each time point t = k, k + 1, \dots, where k (agg_period) is the number of time units (agg_scale) over which to aggregate the time series (x), and f (agg_fun) is the function used to perform the aggregation. The first k - 1 values of the aggregated time series are returned as NA.

By default, agg_fun = "sum", meaning the aggregation results in accumulations over the aggregation period:

\tilde{x}_{t} = \sum_{k=1}^{K} x_{t - k + 1}.

Alternative functions can also be used. For example, specifying agg_fun = "mean" returns the mean over the aggregation period,

\tilde{x}_{t} = \frac{1}{K} \sum_{k=1}^{K} x_{t - k + 1},

while agg_fun = "max" returns the maximum over the aggregation period,

\tilde{x}_{t} = \text{max}(\{x_{t}, x_{t-1}, \dots, x_{t - k + 1}\}).

agg_period is a single numeric value specifying over how many time units the data x is to be aggregated. By default, agg_period is assumed to correspond to a number of days, but this can also be specified manually using the argument agg_scale. timescale is the timescale of the input data x. By default, this is also assumed to be "days".

Since the time series x aggregates data over the aggregation period, problems may arise when x contains missing values. For example, if interest is on daily accumulations, but 50% of the values in the aggregation period are missing, the accumulation over this aggregation period will not be accurate. This can be controlled using the argument na_thres. na_thres specifies the percentage of NA values in the aggregation period before a NA value is returned. i.e. the proportion of values that are allowed to be missing. The default is na_thres = 10.

Value

An xts time series with aggregated values.

Author(s)

Sam Allen, Noelia Otero

Examples

data(data_supply, package = "SEI")

# consider hourly German energy production data in 2019
supply_de <- subset(data_supply, country == "Germany", select = c("date", "PWS"))
supply_de <- xts::xts(supply_de$PWS, order.by = supply_de$date)

# daily accumulations
supply_de_daily <- aggregate_xts(supply_de, timescale = "hours")

# weekly means
supply_de_weekly <- aggregate_xts(supply_de, agg_scale = "weeks",
                                  agg_fun = "mean", timescale = "hours")

plot(supply_de, main = "Hourly energy production")
plot(supply_de_daily, main = "Daily accumulated energy production")
plot(supply_de_weekly, main = "Weekly averaged energy production")

SEI documentation built on Sept. 11, 2024, 5:31 p.m.