Time series models

Time series models

Systems that examine series of historical data; sometimes used as a means of technical forecasting, by examining moving averages.
References in periodicals archive ?
Based on electives, graduates may also be able to construct robust processes and products, perform advanced experimental design techniques, create time series models, study multivariate relationships, or investigate reliability of products.
Moreover, as there have been many different time series models for prediction, it will be of genuine interest to evaluate the best suitability of a model for the prediction of epidemic incidence.
The text includes 10 chapters, most of which are devoted at least in part to discussions about regression or time series models, the bread-and-butter tools for most economists.
Incorporating the underlying inflation trend into standard statistical time series models using the technique laid out in this Commentary is simple and easy.
All our previous models can be classified as statistical model assigned to one of two groups: Time Series Models and descriptive models.
The Box and Jenkins methodology for building time series models includes four phases (Box, Jenkins 1976): (1) model identification, (2) model estimation, (3) model validation, and (4) model forecasting.
2]O emissions from soils under a selection of subtropical horticultural crops (custard apple, mango, and pineapple crops) and apply time series models to describe the influence of environmental factors (soil water content and soil temperature) on the emissions of [N.
The software is designed to help students understand time series models and analyze data.
and Jitendra Kumar (2007), Bayesian Unit Root Test for Time Series Models with Structural Breaks, American Journal of Mathematical and Management Sciences, Vol.
Autoregressive integrated moving-average (ARIMA) models are simple time series models that can be used to fit and forecast univariate data such as fisheries landings.
Although the main bulk of Clive's work on time series models had to do with conditional mean models, he was also interested in the conditional variance.
The developed computational method provides a better approach to overcome the drawback of existing high- order fuzzy time series models.