Diagnostic Summary for Time Series Objects

Provides a compact diagnostic summary for univariate ts objects, extending classical descriptive statistics with key time series diagnostics.

Usage

# S3 method for class 'ts'
desc(x, maxLag = 12, main = NULL, plotit = NULL, verbose = NULL, ...)

# S3 method for class 'Desc.ts'
print(x, digits = NULL, ...)

# S3 method for class 'Desc.ts'
plot(x, ...)

Arguments

x

a univariate object of class "ts"

maxLag

number of lags used in the Ljung-Box test; defaults to 12

main

character string, NULL, or NA, defining the main title. By default (main = NULL) the title will be composed as: (<class(es)>). If NA, no title is printed.

plotit

logical. Should a plot be created? The plot type depends on the classes of the variables. Default can be defined by the option plotit, if it does not exist then it's set to TRUE.

verbose

integer controlling verbosity of table output. One of 1 (minimal), 2 (default), 3 (extensive). Applies to tables only.

...

further arguments passed to methods

digits

number of digits used to format numeric values

Value

an object of class c("Desc.ts", "Desc") containing the computed statistics

Details

The function reports:

• Lag-1 autocorrelation

• Ljung-Box test for overall autocorrelation

• Augmented Dickey-Fuller (ADF) test

• KPSS test

• Linear trend estimation (slope and p-value)

• Suggested Box-Cox transformation parameter

The goal is to provide quick diagnostic guidance before model fitting (e.g., ARIMA specification).

Stationarity is evaluated using both the Augmented Dickey-Fuller (ADF) and KPSS tests. A combined decision rule is used: the series is considered stationary if the ADF test rejects the null hypothesis of a unit root (p < 0.05) and the KPSS test does not reject the null hypothesis of stationarity (p > 0.05).

The Box-Cox transformation parameter is estimated using boxCoxLambda().

References

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time Series Analysis: Forecasting and Control.

Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice.

See also

stats::acf, stats::Box.test, boxCoxLambda, lumen::adfTest, lumen::kpssTest, pharos::plotTimeSeries

Other desc: desc(), desc.Date(), desc.factor(), desc.nn, desc.nq, desc.numeric(), desc.qn, desc.qq, desc.table()

Examples

desc(AirPassengers)
#> Warning: p-value smaller than reported p-value
#> ────────────────────────────────────────────────────────────────────────────── 
#> AirPassengers (ts)
#> 
#> Warning: number of columns of result is not a multiple of vector length (arg 1)
#>  start      end  frequency          
#>    144      118          0  144  118
#>                                     
#>                                     
#>  start      end  frequency          
#> 1949-1  1960-12         12          
#>                                     

desc(Nile, maxLag = 10)
#> Warning: p-value smaller than reported p-value
#> ────────────────────────────────────────────────────────────────────────────── 
#> Nile (ts)
#> 
#> Warning: number of columns of result is not a multiple of vector length (arg 1)
#>  start     end  frequency         
#>    100      85          0  100  85
#>                                   
#>                                   
#>  start     end  frequency         
#> 1871-1  1970-1          1         
#>