ATA.Seasonality {ATAforecasting}R Documentation

Seasonality Tests for The ATAforecasting

Description

This function is a class of seasonality tests using corrgram_test from ATAforecasting package, ndiffs and nsdiffs functions from forecast package. Also, this function is modified version of ndiffs and nsdiffs written by Hyndman et al. forecast package. Please review manual and vignette documents of latest forecast package. According to forecast package, ndiffs and nsdiffs functions to estimate the number of differences required to make a given time series stationary. ndiffs uses unit root tests to determine the number of differences required for time series to be made trend stationary. Several different tests are available:

nsdiffs uses seasonal unit root tests to determine the number of seasonal differences required for time series to be made stationary. Several different tests are available:

Usage

ATA.Seasonality(input, ppy, attr_set)

Arguments

input

The data.

ppy

Frequency of the data.

attr_set

Assign from ATA.SeasAttr function. Attributes set for unit root, seasonality tests.

Value

TRUE if the serie has seasonality. Otherwise, FALSE.

Author(s)

Ali Sabri Taylan and Hanife Taylan Selamlar

References

#'Dickey DA, Fuller WA (1979). “Distribution of the Estimators for Autoregressive Time Series With a Unit Root.” Journal of the American Statistical Association, 74(366), 427–431.

#'Said SE, Dickey DA (1984). “Testing for Unit Roots in Autoregressive-Moving Average Models of Unknown Order.” Biometrika, 71(3), 599–607.

#'Dickey DA, Hasza DP, Fuller WA (1984). “Testing for Unit Roots in Seasonal Time Series.” Journal of the American Statistical Association, 79(386), 355–367.

#'Phillips PCB, Perron P (1988). “Testing for a Unit Root in Time Series Regression.” Biometrika, 75(2), 335–346.

#'Osborn DR, Chui APL, Smith J, Birchenhall CR (1988). “Seasonality and the order of integration for consumption.” Oxford Bulletin of Economics and Statistics, 50(4), 361–377.

#'Hylleberg S, Engle RF, Granger CWJ, Yoo BS (1990). “Seasonal integration and cointegration.” Journal of Econometrics, 1344(1), 215–238.

#'Kwiatkowski D, Phillips P, Schmidt P, Shin Y (1992). “Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?” Journal of Econometrics, 54(1–3), 159–178.

#'Canova F, Hansen BE (1995). “Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability.” Journal of Business and Economic Statistics, 13(3), 237–252.

#'Wang X, Smith KA, Hyndman RJ (2006). “Characteristic-based clustering for time series data.” Data Mining and Knowledge Discovery, 13(3), 335–364.

See Also

forecast, urca, tseries, uroot, stlplus, stR, stl, decompose, tbats, seasadj.


[Package ATAforecasting version 0.0.60 Index]