Description Usage Arguments Value Note Author(s) Examples

Determine the appropriate power transformation for time-series data. The objective is to estimate the power transformation so that the transformed time series is approximately a Gaussian AR process.

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`y` |
univariate time series (must be positive) |

`order` |
AR order for the data; if missing, the order is determined by AIC for the log-transformed data |

`lambda` |
a vector of candidate power transformation values; if missing, it is set to be from -2 to 2, with increment .01 |

`plotit` |
logical value, if true, plot the profile log-likelihood for the power estimator |

`method` |
method of AR estimation; default is "mle" |

`...` |
other parameters to be passed to the ar function |

A list that contains the following:

`lambda` |
candidate power transformation parameter values |

`loglike` |
profile log-likelihood |

`mle` |
maximum likelihood estimate of the power transformation value |

`ci` |
95% C.I. of the power transformation value |

The procedure is very computer intensive. Be patient for the outcome

Kung-Sik Chan

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TSA documentation built on July 2, 2018, 1:04 a.m.

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