This function is included in ts.analysis function and aims to extract the ACF and PACF details of the input time series data and the ACF, PACF of the residuals after fitting an Arima model.
ts.acf(tsdata, model_residuals, a=0.95, tojson=F)
| tsdata | The input univariate time series data |
|---|---|
| model_residuals | The model's residuals after fitting a model to the time series |
| a | The significant level (default a=0.95) |
| tojson | If TRUE the results are returned in json format, default returns a list |
A list with the parameters:
acf.parameters:
acf The estimated acf values of the input time series
acf.lag The lags at which the acf is estimated
confidence.interval.up The upper limit of the confidence interval
confidence.interval.low The lower limit of the confidence interval
pacf.parameters:
pacf The estimated pacf values of the input time series
pacf.lag The lags at which the pacf is estimated
confidence.interval.up The upper limit of the confidence interval
confidence.interval.low The lower limit of the confidence interval
acf.residuals.parameters:
acf.res The estimated acf values of the model residuals
acf.res.lag The lags at which the acf is estimated of the model residuals
confidence.interval.up The upper limit of the confidence interval
confidence.interval.low The lower limit of the confidence interval
pacf.residuals.parameters:
pacf.res The estimated pacf values of the model residuals
pacf.res.lag The lags at which the pacf is estimated of the model residuals
confidence.interval.up The upper limit of the confidence interval
confidence.interval.low The lower limit of the confidence interval
This function is used internally in ts.analysis function and the output is a list with grouped ACF and PACF parameters of the input time series data, as well as the ACF and PACF parameters of the residuals needed for the graphical purposes in OBEU.
ts.analysis, Acf, Pacf
ts.acf(Athens_draft_ts)#> $acf.parameters #> $acf.parameters$acf #> [1] 1.00000000 0.67010083 0.20480616 -0.29189364 -0.51269294 -0.51430701 #> [7] -0.28195630 -0.01821677 0.10932753 0.09101177 0.03254134 #> #> $acf.parameters$acf.lag #> [1] 0 1 2 3 4 5 6 7 8 9 10 #> #> $acf.parameters$confidence.interval.up #> [1] 0.5657929 #> #> $acf.parameters$confidence.interval.low #> [1] -0.5657929 #> #> #> $pacf.parameters #> $pacf.parameters$pacf #> [1] 0.67010083 -0.44327501 -0.43580079 0.02709037 -0.11788115 0.01627274 #> [7] -0.04326813 -0.27712013 -0.12208959 0.05034829 #> #> $pacf.parameters$pacf.lag #> [1] 1 2 3 4 5 6 7 8 9 10 #> #> $pacf.parameters$confidence.interval.up #> [1] 0.5657929 #> #> $pacf.parameters$confidence.interval.low #> [1] -0.5657929 #> #> #> $acf.residuals.parameters #> NULL #> #> $pacf.residuals.parameters #> NULL #>