Contents
How is the Box-Cox transformation used in time series?
The Box-Cox transformation is a family of power transformations indexed by a parameter lambda. Whenever you use it the parameter needs to be estimated from the data. In time series the process could have a non-constant variance. if the variance changes with time the process is nonstationary.
Why do we use Box Cox in regression?
Regarding other possible uses of Box-Cox in the case of a series of iid random variables that do not appear to be normally distributed there may be a particular value of lambda that makes the data look approximately normal. Presumably this could be applied in regression or time series to the error term.
How is the exogenous variable used in time series?
In time series, the exogenous variable is a parallel time series that are not modeled directly but is used as a weighted input to the model. The method is suitable for univariate time series with trend and/or seasonal components and exogenous variables.
What is the Lambda of Box-Cox transformation?
TBATS has chosen to use Box-Cox transformation with lambda of 0.234955. Trend is not being modeled and ARMA is not used to model residuals as p, q are 0.
Is the book forecasting with Box Cox free?
More importantly, the textbook is free. The authors of Forecasting devote one sub-chapter to transforming data (Section 3.2: “Transformations and Adjustments”), where they go over four types of transformations. One of these transformations (and the first I was introduced to in undergrad) is the Box-Cox Transformation.
How to write confidence interval for Box Cox?
If llf is the log-likelihood function, then the confidence interval for lambda can be written as From Scipy documentation on Box-Cox function. where X² is the chi-squared distribution. (It may be unnecessary to transform your data if the confidence interval includes 1). Next, fit your model to the Box-Cox transformed data.