Exploring Models for the Growth of Literature Data

Abstract

Any time series is forecasted using a suitable model based on the analysis of historical data. The value of a model lies in the efficacy with which it performs the task for which it has been constructed. A model is considered good if it fits the data well.
In other words, models should have good parameter values and fit statistics. Many researchers have successfully applied various statistical models to analyse the growth of literature data. However, there is no generalised rule or procedure put forwarded by these researchers. The question, therefore, arises as to how one compares the appropriateness of different type of models fitted to the data? The aim of this paper is to forecast the time series of growth of literature data. Two different approaches to probe this kind of data have been applied. These approaches are the multiplicative seasonal model approach and nonlinear model approach where the trend has an exponential growth form. It has been shown that there is plethora of models that come out with good fit parameters. This communication thus highlights some basic issues related to forecast of growth of literature data.

http://dx.doi.org/10.14429/djlit.27.3.131