Time series is an important part of decision making and several results are constructed on estimates of forthcoming events. In the Time series process, since, for the coming actions include probability; the predictions are frequently not perfect. The objectives of the Time series are to decrease the prediction error; to create predictions that are rarely improper and that have minor prediction mistakes. In selecting a suitable Time series model, the researcher wants to be responsive that numerous altered models may have comparable properties. A good model will fit the data well. The goodness of fit recovers as additional limits are involved in the model. This arises a problem with the ARMA (p, q) models, because p and q take low values. It should be noted that the best model fit need not imply to provide best forecasts. There exists several model selection criteria that trade off a decrease in the sum of squares of the errors for a more sparing model. A goodness of fit criterion for ARMA (p, q) model and modified selection criteria for Time series models have been suggested in the present study.

Goodness of fit and model selection criteria for time series models

The MANOVA and MANCOVA are the extensions of the univariate ANOVA and ANCOVA techniques to multidimensional or vector valued observations. The assumption of a Gaussian distribution has been replaced with the Multivariate Gaussian distribution for the vectors data and residual term variables in the statistical models of these techniques. The objective of MANCOVA is to determine if there are statistically reliable mean differences that can be demonstrated between groups later modifying the newly created variable. When randomization assignment of samples or subjects to groups is not possible, multivariate analysis of covariance (MANCOVA) provides statistical matching of groups by adjusting dependent variables as if all subjects scored the same on the covariates. In this research article, an extension has been made to the MANCOVA technique with more number of covariates and homogeneity of regression coefficient vectors is also tested.

https://iopscience.iop.org/article/10.1088/1757-899X/263/4/042134/pdf

G Y Mythili

Papers

Goodness of fit and model selection criteria for time series models

MANCOVA for one way classification with homogeneity of regression coefficient vectors