An Interactive Visualization of the ACF and PACF Functions

ts_cor(ts.obj, type = "both", seasonal = TRUE, ci = 0.95,
lag.max = NULL, seasonal_lags = NULL)

## Arguments

ts.obj A univariate time series object class 'ts' A character, defines the plot type - 'acf' for ACF plot, 'pacf' for PACF plot, and 'both' (default) for both ACF and PACF plots A boolean, when set to TRUE (default) will color the seasonal lags The significant level of the estimation - a numeric value between 0 and 1, default is set for 0.95 maximum lag at which to calculate the acf. Default is 10*log10(N/m) where N is the number of observations and m the number of series. Will be automatically limited to one less than the number of observations in the series A vector of integers, highlight specific cyclic lags (besides the main seasonal lags of the series). This is useful when working with multiseasonal time series data. For example, for a monthly series (e.g., frequency 12) setting the argument to 3 will highlight the quarterly lags

## Examples


data(USgas)

ts_cor(ts.obj = USgas)

# Setting the maximum number of lags to 72
ts_cor(ts.obj = USgas, lag.max = 72)

# Plotting only ACF
ts_cor(ts.obj = USgas, lag.max = 72, type = "acf")#> Warning: Ignoring 72 observations#> Warning: Ignoring 67 observations#> Warning: Ignoring 7 observations