Estimate autocorrelations by time series

Estimate the sample autocorrelation function for one or more time series in a tibble.

Usage

estimate_acf(.data, context, lag_max = 24, level = 0.9, ...)

Arguments

.data

A tibble containing the time series data.

context

A named list with the identifiers for series_id, value_id, and index_id.

lag_max

Integer. Maximum lag for which the autocorrelation is estimated.

level

Numeric value. Confidence level used to calculate the approximate significance bound.

...

Further arguments passed to stats::acf().

Value

A tibble with the series identifier and the columns type, lag, value, bound, and sign.

Details

estimate_acf() groups the input data by the series identifier supplied in context and estimates the sample autocorrelation function for each time series separately.

The output contains one row per series and lag. The column bound contains an approximate significance threshold based on the selected confidence level. The logical column sign indicates whether the absolute autocorrelation is larger than this threshold.

See also

Other data analysis: acf_vec(), estimate_kurtosis(), estimate_mode(), estimate_pacf(), estimate_skewness(), pacf_vec(), summarise_data(), summarise_split(), summarise_stats()

Examples

library(dplyr)

context <- list(
  series_id = "series",
  value_id = "value",
  index_id = "index"
)

data <- M4_monthly_data |>
  filter(series %in% c("M23100", "M14395"))

estimate_acf(
  .data = data,
  context = context,
  lag_max = 12
)
#> # A tibble: 24 × 6
#>    series type    lag  value bound sign 
#>    <chr>  <chr> <int>  <dbl> <dbl> <lgl>
#>  1 M14395 ACF       1 0.675  0.121 TRUE 
#>  2 M14395 ACF       2 0.410  0.121 TRUE 
#>  3 M14395 ACF       3 0.253  0.121 TRUE 
#>  4 M14395 ACF       4 0.129  0.121 TRUE 
#>  5 M14395 ACF       5 0.0847 0.121 FALSE
#>  6 M14395 ACF       6 0.0448 0.121 FALSE
#>  7 M14395 ACF       7 0.0246 0.121 FALSE
#>  8 M14395 ACF       8 0.0732 0.121 FALSE
#>  9 M14395 ACF       9 0.149  0.121 TRUE 
#> 10 M14395 ACF      10 0.282  0.121 TRUE 
#> # ℹ 14 more rows