prewhitened_ccf: Calculate cross-correlation coefficients for prewhitened time...

In ercbk/ebtools: Eric's Data Science Toolbox

Calculate cross-correlation coefficients for prewhitened time series

Description

prewhitened_ccf() prewhitens time series, calculates cross-correlation coefficients, and returns statistically significant values.

Usage

prewhitened_ccf(
  input,
  output,
  input_col,
  output_col,
  keep_input = "both",
  keep_ccf = "both",
  max.order
)

Arguments

input	tsibble; The influential or "predictor-like" time series
output	tsibble; The affected or "response-like" time series
input_col	string; Name of the numeric column of the input tsibble
output_col	string; Name of the numeric column of the output tsibble
keep_input	string; values: "input_lags", "input_leads" or "both"; Default is "both".
keep_ccf	string; values: "positive", "negative", or "both; Default is "both"
max.order	integer; The maximum lag used in the CCF calculation.

Details

In a cross-correlation in which the direction of influence between two time-series is hypothesized or known,

• The influential time-series is called the "input" time-series

• The affected time-series is called the "output" time-series

The cross-correlation function calculates correlation values between lags and leads of the input series and the output series. Sometimes only correlations between the leads or lags of the input series and the output series make theoretical sense, or only positive or negative correlations make theoretical sense.

• The "keep_input" argument specifies whether you want to keep only output CCF values involving leads or lags of the input series or both.

• The "keep_ccf" argument specifies whether you want to keep only output positive, negative, or both CCF values.

prewhitened_ccf differences the series if it's needed, prewhitens, and outputs either statistically significant values of the CCF or the top non-statistically significant value if no statistically significant values are found. The prewhitening method that is used is from Cryer and Chan (2008, Chapter 11).

Value

A tibble with the following columns:

• input_type: "lag" or "lead"

• input_series: lag or lead number

• signif_type: "Statistically Significant" or "Not Statistically Significant"

• signif_threshold: Threashold CCF value for statistical significance at the 95% level

• ccf: Calculated ccf value

References

Cryer, Jonathan, and Chan, Kung-Sik. 2008. Time Series Analysis With Applications in R. New York: Springer Science+Business Media (pp. 260-271)

Examples

oh_cases <- ohio_covid %>%
   dplyr::select(date, cases) %>%
   tsibble::as_tsibble(index = date)

oh_deaths <- ohio_covid %>%
   dplyr::select(date, deaths_lead0) %>%
   tsibble::as_tsibble(index = date)

oh_ccf_tbl <- prewhitened_ccf(input = oh_cases,
                              output = oh_deaths,
                              input_col = "cases",
                              output_col = "deaths_lead0",
                              max.order = 40,
                              keep_input = "input_lag",
                              keep_ccf = "positive")

oh_ccf_tbl


library(dplyr, warn.conflicts = FALSE)

reg_cases_tsb <- us_regional_cases %>%
  tsibble::as_tsibble(index = date, key = region) %>%
  tsibble::group_by_key() %>%
  tidyr::nest() %>%
  arrange(region) %>%
  ungroup() %>%
  mutate(id = as.character(row_number()))

reg_deaths_tsb <- us_regional_deaths %>%
  tsibble::as_tsibble(index = date, key = region) %>%
  tsibble::group_by_key() %>%
  tidyr::nest() %>%
  arrange(region) %>%
  ungroup() %>%
  mutate(id = as.character(row_number()))

reg_ccf_vals <- purrr::map2_dfr(reg_cases_tsb$data,
                                reg_deaths_tsb$data,
                                prewhitened_ccf,
                                input_col = "reg_sev_day_cases",
                                output_col = "reg_sev_day_deaths",
                                max.order = 40,
                                .id = "id") %>%
  left_join(reg_cases_tsb %>%
             select(id, region), by = "id")

head(reg_ccf_vals)

ercbk/ebtools documentation built on Feb. 22, 2025, 1:51 p.m.