This paper compares the structure of three models for estimating future growth in a time series. It is shown that a regression model gives minimum weight to the last observed growth and maximum weight to the observed growth in the middle of the sample period. A first-order integrated ARIMA model, or
Evolving Time Series Forecasting ARMA Models
✍ Scribed by Paulo Cortez; Miguel Rocha; José Neves
- Book ID
- 111584194
- Publisher
- Springer US
- Year
- 2004
- Tongue
- English
- Weight
- 196 KB
- Volume
- 10
- Category
- Article
- ISSN
- 1381-1231
No coin nor oath required. For personal study only.
📜 SIMILAR VOLUMES
In this paper multivariate ARMA models are applied to the problem of forecasting city budget variables. Unlike univariate time-series methods, multivariate models can use relationships among budget variables as well as relationships with economic and demographic indicators. Although available budget
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