Accurate forecasting of demand under uncertain environment is one of the vital tasks for improving supply chain activities because order amplification or bullwhip effect (BWE) and net stock amplification (NSAmp) are directly related to the way the demand is forecasted. Improper demand forecasting results in increase in total supply chain cost including shortage cost and backorder cost. However,...

Network traffic modeling significantly affects various considerations in networking, including network resource allocation, quality of service provisioning, network traffic management, congestion control, and bandwidth efficiency. These are very important issues in network protocol design, too. In this paper, a comprehensive comparison of modeling approaches of adaptive neuro fuzzy inference sy...

The importing-and-exporting goods normally requires organizational cooperation among different international sectors ranging from purchasing, manufacturing, transporting, inventory, to distribution centers. For having their smooth coordination in the international trade, accurately forecasting the volume of imported-exported goods has become the core issue. This paper aims to obtain efficient f...

The most destructive enemy of the lychee, Litchi chinensis Sonn. (Sapindales: Sapindaceae), in India is a stink bug, Tessaratoma papillosa (Drury) (Hemiptera: Tessaratomidae). The population of T. papillosa on lychee trees varied from 1.436 0.501 to 9.856 3.924 insects per branch in this study. An increase in the temperature and a decrease in the relative humidity during summer months (April to...

Yağış ve akış gibi hidrolojik verilerin tahmini için farklı modellerin geliştirilmesi gelecekte su ile ilgili problemlerle mücadele edebilmek açısından önemlidir. Bu çalışma, Yapay Sinir Ağı (ANN), Otoregresif Bütünleşik Hareketli Ortalama (ARIMA), Dalgacık-ARIMA (WARIMA) WARIMA-ANN modellerinin aylık akım tahmin performanslarını araştırmaktadır. modeller, Türkiye’nin Susurluk havzasındaki iki ...

This paper discusses the prediction of inflation rate in Indonesia. The data used this research is assumed to have both linear and non-linear components. ARIMA model selected accommodate component, while ANFIS method accounts for component data. Thus, known as hybrid ARIMA-ANFIS model. clustering performed using Fuzzy C-Mean (FMS) with a Gaussian membership function. Consider 2 6 clusters. opti...

The purpose of this work is discovering regularities in financial time series using Inductive Logic Programming (ILP) and related "Discovery" software system [Vityaev et al., 1992,1993] in data mining. Discovered regularities were used for forecasting the target variable, representing the relative difference in percent between today's closing price and the price five days ahead. We describe the...

Many researchers have used time series models to construct population forecasts and prediction intervals at the national level, but few have evaluated the accuracy of their forecasts or the out-of-sample validity of their prediction intervals. Fewer still have developed models for subnational areas. In this study, we develop and evaluate six ARIMA time series models for states in the United Sta...

Since malaria is prevalent in less developed and more remote areas in which public health resources are often scarce, targeted intervention is essential in allocating resources for effective malaria control. To effectively support targeted intervention, predictive models must be not only accurate but they must also have high temporal and spatial resolution to help determine when and where to in...

Short-term forecast of pertussis incidence is helpful for advanced warning and planning resource needs for future epidemics. By utilizing the Auto-Regressive Integrated Moving Average (ARIMA) model and Exponential Smoothing (ETS) model as alterative models with R software, this paper analyzed data from Chinese Center for Disease Control and Prevention (China CDC) between January 2005 and June 2...