forecast: nsdiffs – R documentation

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nsdiffs

Number of differences required for a seasonally stationary series

Description

Functions to estimate the number of differences required to make a given time series stationary. nsdiffs estimates the number of seasonal differences necessary.

Usage

nsdiffs(
  x,
  alpha = 0.05,
  m = frequency(x),
  test = c("seas", "ocsb", "hegy", "ch"),
  max.D = 1,
  ...
)

Arguments

`x`	A univariate time series
`alpha`	Level of the test, possible values range from 0.01 to 0.1.
`m`	Deprecated. Length of seasonal period
`test`	Type of unit root test to use
`max.D`	Maximum number of seasonal differences allowed
`...`	Additional arguments to be passed on to the unit root test

Details

nsdiffs uses seasonal unit root tests to determine the number of seasonal differences required for time series x to be made stationary (possibly with some lag-one differencing as well).

Several different tests are available:

If test="seas" (default), a measure of seasonal strength is used, where differencing is selected if the seasonal strength (Wang, Smith & Hyndman, 2006) exceeds 0.64 (based on minimizing MASE when forecasting using auto.arima on M3 and M4 data).
If test="ch", the Canova-Hansen (1995) test is used (with null hypothesis of deterministic seasonality)
If test="hegy", the Hylleberg, Engle, Granger & Yoo (1990) test is used.
If test="ocsb", the Osborn-Chui-Smith-Birchenhall (1988) test is used (with null hypothesis that a seasonal unit root exists).

Value

An integer indicating the number of differences required for stationarity.

Author(s)

Rob J Hyndman, Slava Razbash and Mitchell O'Hara-Wild

References

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

Osborn DR, Chui APL, Smith J, and Birchenhall CR (1988) "Seasonality and the order of integration for consumption", Oxford Bulletin of Economics and Statistics 50(4):361-377.

Canova F and Hansen BE (1995) "Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability", Journal of Business and Economic Statistics 13(3):237-252.

Hylleberg S, Engle R, Granger C and Yoo B (1990) "Seasonal integration and cointegration.", Journal of Econometrics 44(1), pp. 215-238.

Examples

nsdiffs(AirPassengers)

forecast

Forecasting Functions for Time Series and Linear Models

v8.14

GPL-3

Authors

Rob Hyndman [aut, cre, cph] (<https://orcid.org/0000-0002-2140-5352>), George Athanasopoulos [aut], Christoph Bergmeir [aut] (<https://orcid.org/0000-0002-3665-9021>), Gabriel Caceres [aut], Leanne Chhay [aut], Mitchell O'Hara-Wild [aut] (<https://orcid.org/0000-0001-6729-7695>), Fotios Petropoulos [aut] (<https://orcid.org/0000-0003-3039-4955>), Slava Razbash [aut], Earo Wang [aut], Farah Yasmeen [aut] (<https://orcid.org/0000-0002-1479-5401>), R Core Team [ctb, cph], Ross Ihaka [ctb, cph], Daniel Reid [ctb], David Shaub [ctb], Yuan Tang [ctb] (<https://orcid.org/0000-0001-5243-233X>), Zhenyu Zhou [ctb]

Initial release