๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Time-series analysis supported by power transformations

โœ Scribed by Victor M. Guerrero

Publisher
John Wiley and Sons
Year
1993
Tongue
English
Weight
648 KB
Volume
12
Category
Article
ISSN
0277-6693

No coin nor oath required. For personal study only.

โœฆ Synopsis

Abstract

This paper presents some procedures aimed at helping an applied timeโ€series analyst in the use of power transformations. Two methods are proposed for selecting a varianceโ€stabilizing transformation and another for biasโ€reduction of the forecast in the original scale. Since these methods are essentially modelโ€independent, they can be employed with practically any type of timeโ€series model. Some comparisons are made with other methods currently available and it is shown that those proposed here are either easier to apply or are more general, with a performance similar to or better than other competing procedures.

๐Ÿ“œ SIMILAR VOLUMES

On Selecting a Power Transformation in T
On Selecting a Power Transformation in Time-Series Analysis
โœ Cathy W. S. Chen; Jack C. Lee ๐Ÿ“‚ Article ๐Ÿ“… 1997 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 214 KB

The primary aim of this paper is to select an appropriate power transformation when we use ARMA models for a given time series. We propose a Bayesian procedure for estimating the power transformation as well as other parameters in time series models. The posterior distributions of interest are obtai

Time series forecasting models involving
Time series forecasting models involving power transformations
โœ W. S. Hopwood; J. C. McKeown; P. Newbold ๐Ÿ“‚ Article ๐Ÿ“… 1984 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 338 KB

In this paper we discuss procedures for overcoming some of the problems involved in fitting autoregressive integrated moving average forecasting models to time series data, when the possibility of incorporating an instantaneous power transformation of the data into the analysis is contemplated. The

Moments of power transformed time series
Moments of power transformed time series
โœ Richard W. Katz ๐Ÿ“‚ Article ๐Ÿ“… 1999 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 94 KB ๐Ÿ‘ 1 views

A simple recursion is presented for calculating moments (e.g., mean, variance, and autocorrelation function) of a time series that has been power transformed to normality. Its derivation is elementary, relying on the moment-generating function for a bivariate normal distribution. To make clear the d

Forecasting economic time series that re
Forecasting economic time series that require a power transformation: Case of state tax receipts
โœ Nader Nazmi; Jane H. Leuthold ๐Ÿ“‚ Article ๐Ÿ“… 1988 ๐Ÿ› John Wiley and Sons ๐ŸŒ English โš– 657 KB

In this study, time series analysis is applied to the problem of foreca\tin: state income tax receipts. The data series is of special interest since it exhibits a strong trend with a high multiplicative seasonal component. An appropriate model is identified by simultaneous estimation of the paramete