Proper models for prediction of time series data can be an advantage in making important decisions. In this study, we tried with the comparison between one of the most useful classic models of economic evaluation, auto-regressive integrated moving average model and one of the most useful artificial intelligence models, adaptive neuro-fuzzy inference system (ANFIS), investigate modeling procedur...

The object of this paper is to study the asymptotic dependence structure of the linear time series models with infinitely divisible innovations by the use of their characteristic functions. Autoregressive moving-average (ARMA) models and fractional autoregressive integrated moving-average (FARIMA) models are analyzed. As examples of infinitely divisible innovations, the class of radially absolu...

The changes in hydraulic geometry of the Hwang River downstream of Hapcheon Dam have been investigated since 1982. The study reach is 45 km long from the Hapcheon Re-regulation Dam to the confluence with the Nakdong River, in South Korea. The geographic information system (GIS) analysis of a time series of aerial photographs taken in 1982, 1993 and 2004 showed that the non-vegetated active chan...

BACKGROUND The Orphan Drug Act (1983) established several incentives to encourage the development of orphan drugs (ODs) to treat rare diseases and conditions. This study analyzed the characteristics of OD designations, approvals, sponsors, and evaluated the effective patent and market exclusivity life of orphan new molecular entities (NMEs) approved in the US between 1983 and 2007. METHODS Pr...

This note is concerned with the asymptotic properties of pairwise likelihood estimation procedures for linear time series models. The latter includes ARMA as well as fractionally integrated ARMA processes, where the fractional integration parameter d < 0.5. In some cases, including AR(1) processes and longmemory processes with d < 0.25, the loss in efficiency in using pairwise likelihood method...

This paper presents a method of forecasting time series with multiple seasonal cycles based on Generalized Regression Neural Network. This is a memory-based, fast learned and easy tuned type of neural network. The time series is preprocessed to define input and output patterns of seasonal cycles, which simplifies the forecasting problem. The method is useful for forecasting nonstationary time s...

This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the ...

Online policy discussion forums can be construed as a social network where citizens interact and collectively deliberate on policy issues. It is believed that citizen participation in such forums can help in the formulation of well-deliberated policy solutions. A key concern for governments is to promote participation in such forums. Social network literature reveals that the evolving interacti...

studies of rainfall variation generally focus on large areas. for example, in india, the area average monsoon rainfall series of the whole country or meteorological subdivisions are used. this would be of no use for local agriculture, particularly in places where rainfall is very high or very low, especially for crops like small cardamom and vanilla which are very sensitive to soil moisture and...