Abstract
Effective time-series analysis is based on the assumption that the series under investigation is a realisation of a "stationary" stochastic process. In practice, such a stable series can generally only be obtained after some appropriate transformation of the raw data. Two types of non-stationarity can be removed by, respectively, linear and non-linear transformation. These are "homogeneous" non-stationarity and variance instability. The first can be dealt with by backshift operator methods, whilst the second is usually carried out by the approach of Box and Cox, though an easier way is given. The loss of optimal properties, on transforming back to the original situation, can be offset by suitably biasing the results.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
