Traditionally, the autoregressivemoving average (ARMA)model has been one of themost widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superior...

Extended Thermodynamics of dense gases is characterized by two hierarchies of field equations, which allow one to overcome some restrictions on the generality of the previous models. This idea has been introduced by Arima, Taniguchi, Ruggeri and Sugiyama. In the case of a 14-moment model, they have found the closure of the balance equations up to second order with respect to equilibrium. Here, ...

This paper presents an overview of and introduction to some of the standard time series modeling and forecasting techniques as implemented in SAS with PROC ARIMA and PROC AUTOREG, among others. Examples are presented to illustrate the concepts. In addition to a few initial ARIMA examples, more sophisticated modeling tools will be addressed. Included will be regression models with time series er...

This paper presents the use of times series AutoRegressive Integrated Moving Average ARIMA(p,d,q) model with interventions, and neural network back-propagation model in analyzing the behavior of sales in a medium size enterprise located in Rio Grande do Sul Brazil for the period January 1984 – December 2000. The forecasts obtained using the neural network back-propagation model were found to be...

We briefly review various mappings of fermion pairs to bosons, including those based on mapping operators, such as Belyaev-Zelevinskii, and those on mapping states, such as Marumori; in particular we consider the work of Otsuka-Arima-Iachello, aimed at deriving the Interacting Boson Model. We then give a rigorous and unified description of state-mapping procedures which allows one to systematic...

The main objective of this paper is to show potential usefulness of the combination of autoregressive integrated moving average (ARIMA) models and logistic regression with automatic model selection (see our work presented at SUGI’28 and SUGI’29.) Timeseries analysis with ARIMA provides only one perspective of the information in the surveillance data (i.e. the number of patients as a function of...

The price of fresh agricultural products changes up and down recently. In order to accurately forecast the agricultural precuts demand, a forecasting model based on ARIMA is provided in this study. It can be found that asymmetric information and unbalance about supply and demand exist in the market through analyzing the reasons. The ARIMA model for fresh agricultural products can forecast the d...

Streamflow forecasting is very important for water resources management and flood defence. In this paper two forecasting methods are compared: ARIMA versus a multilayer perceptron neural network. This comparison is done by forecasting a streamflow of a Mexican river. Surprising results showed that in a monthly basis, ARIMA has lower prediction errors than this Neural Network. Key-Words: Auto re...

As a versatile investment tool in energy markets for speculators and hedgers, the Goldman Sachs Commodity Index (GSCI) futures are quite well known. Therefore, this paper proposes a hybrid model incorporating ARCH family models and ANN model to forecast GSCI futures price. Empirical results show that the hybrid ARCH(1)-M-ANN model is superior to ARIMA, ARCH(1),GARCH(1,1), EGARCH(1,1) and ARIMA-...

The predictability of network traffic is a significant interest in many domains such as congestion control, admission control, and network management. An accurate traffic prediction model should have the ability to capture prominent traffic characteristics, such as long-range dependence (LRD) and self-similarity in the large time scale, multifractal in small time scale. In this paper we propose...