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

estimates the number of seasonal differences
necessary.

## 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).

## 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.

## See also

`auto.arima`

, `ndiffs`

, `ocsb.test`

, `hegy.test`

, and `ch.test`