Many forecasting models based on the concepts of Fuzzy time series have been proposed in the past decades. These models have been widely applied to various problem domains, especially in dealing with forecasting problems in which historical data are linguistic values. In this paper, we present a new fuzzy time series forecasting model, which uses the historical data as the universe of discourse...

The forecasting problem is one of the main environmental problems that need efficient software tools. More concrete, it can mean meteorological/weather forecasting, air/soil/water pollution forecasting, flood forecasting and so on. Several methods based on artificial intelligence were proposed by taken into account that they can offer more informed methods that use domain specific knowledge, an...

This paper develops an artificial neural network (ANN) model to forecast the optimum level of raw materials inventory as a function of product demand, manufacturing lead-time, supplier reliability, material holding cost, and material cost. The model selects a feed-forward back-propagation ANN with twelve hidden neurons as the optimum network. We test the model with pharmaceutical company data. ...

Many fuzzy time series approaches have been proposed in recent years. These methods include three main phases such as fuzzification, defining fuzzy relationships and, defuzzification. Aladag et al. [2] improved the forecasting accuracy by utilizing feed forward neural networks to determine fuzzy relationships in high order fuzzy time series. Another study for increasing forecasting accuracy was...

Demand forecasting; which sound basis for decision making process, is among the key activities that directly affect the supply chain performance. As the demand pattern varies from system to system, determination of the appropriate forecasting model that best fits the demand pattern is a hard decision in management of supply chains. The whiplash effect can be express as the variability of the de...

  Fuzzy time series have been developed during the last decade to improve the forecast accuracy. Many algorithms have been applied in this approach of forecasting such as high order time invariant fuzzy time series. In this paper, we present a hybrid algorithm to deal with the forecasting problem based on time variant fuzzy time series and particle swarm optimization algorithm, as a highly effi...

Fuzzy time series forecasting methods have been widely studied in recent years. This is because fuzzy time series forecasting methods are compatib le with flexib le calculat ion techniques and they do not require constraints that exist in conventional time series approaches. Most of the real life time series exh ibit periodical changes arising from seasonality. These variations are called seaso...

Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...

Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...

For modeling of change of terminal server load, the approach including representation of time series of server parameters in the form of fuzzy time series is used. Further in the article the analysis of fuzzy time series is considered. The model of fuzzy tendencies is offered for terminal server traffic modeling. This model reflects change of the volume of terminal server traffic expressed ling...