trendtestR (version 1.0.1)

check_rate_diff_arima_ready: Assess Time Series Readiness for ARIMA Modeling / Pruefung der Eignung fuer ARIMA-Zeitreihenmodellierung

Description

This function performs diagnostics on a numeric time series (e.g., rate difference) to evaluate whether ARIMA modeling is appropriate. It runs tests for autocorrelation (Ljung-Box), trend presence, and stationarity (ADF & KPSS), and gives modeling recommendations. Optional visualizations include line plot, ACF/PACF, and STL decomposition.

Usage

check_rate_diff_arima_ready(
  rate_diff_vec,
  date_vec = NULL,
  frequency = 52,
  plot_acf = TRUE,
  do_stl = TRUE,
  verbose = TRUE,
  max_lag_acf = min(3 * frequency, floor(length(rate_diff_vec)/4))
)

Value

A list containing:

ts_data

The cleaned numeric time series

assessment

Overall diagnostic and modeling recommendation

adf

ADF test result (stationarity)

kpss

KPSS test result (stationarity)

plots

Optional ggplot objects (e.g., time series plot)

stationarity_assessment

Summary of stationarity status and differencing recommendation

Arguments

rate_diff_vec

Numeric vector of rate differences. / Numerischer Vektor (z.B. Rate)

date_vec

Optional. Corresponding date vector (used for plotting). / Optionaler Datumsvektor

frequency

Time series frequency (e.g., 52 for weekly). Default is 52. / Frequenz der Zeitreihe

plot_acf

Logical. Whether to compute and plot ACF/PACF. Default is TRUE. / ACF/PACF anzeigen?

do_stl

Logical. Whether to perform and plot STL decomposition. Default is TRUE. / STL-Dekomposition durchfuehren?

verbose

Whether to print standardization info /Ob Statusinformationen ausgegeben werden sollen

max_lag_acf

Max lag to use for ACF plots. Default is min(3 * frequency, floor(length(rate_diff_vec) / 4)). / Max. Verzoegerung fuer ACF

Details

Diese Funktion prueft, ob eine Zeitreihe (z.B. Differenz von Raten) fuer ARIMA-Modelle geeignet ist. Sie fuehrt Autokorrelationspruefung (Ljung-Box), Trendtest, sowie Stationaritaetstests (ADF & KPSS) durch und gibt Modellierungsempfehlungen. Optional werden Zeitreihengrafiken wie Linienplot, ACF/PACF und STL-Dekomposition erstellt.

Examples

vec <- c(NA, rnorm(60, 0.1, 1))
check_rate_diff_arima_ready(vec, frequency = 12)