Recently, the Particle Swarm Optimization (PSO) technique has gained much attention in the field of time series forecasting. Although PSO trained Artificial Neural Networks (ANNs) performed reasonably well in stationary time series forecasting, their effectiveness in tracking the structure of non-stationary data (especially those which contain trends or seasonal patterns) is yet to be justified...

agriculture as one of the major economic sectors of iran, has an important role in gross domestic production by providing about 14% of gdp. this study attempts to forecast the value of the agriculture gdp using periodic autoregressive model (par), as the new seasonal time series techniques. to address this aim, the quarterly data were collected from march 1988 to july 1989. the collected data w...

The article is devoted to the problem of applying the formal data mining tool – forecasting – for the developing of new software and for reengineering the present software. We propose the algorithm adjustments of the time series forecasting. This algorithm takes into account the dependence of the current state of time series from the previous one, the influence of basic fuzzy projected trends i...

Fuzzy approach and artificial neural networks become effective tool for researchers by forecasting fuzzy time series. The relation of these has advantage to improve forecasting performance especially in handling nonlinear systems. Hence, in this study we aimed to handle a nonlinear problem to apply neural network-based fuzzy time series model. Differing from previous studies, we used various de...

This paper presents a fuzzy system approach to the prediction of nonlinear time series and dynamical systems based on a fuzzy model that includes its derivative information. The underlying mechanism governing the time series, expressed as a set of IF–THEN rules, is discovered by a modified structure of fuzzy system in order to capture the temporal series and its temporal derivative information....

The bullwhip effect in nowadays Supply Chains has become a major source of problems and has attracted supply chain scientists attentions. This paper explores the concept of bullwhip effect in supply chains throughout a completely new approach. Assuming all demands are fuzzy in supply chain, fuzzy If-Then rules are used to show the bullwhip effect. Application of fuzzy logic is due to the fuzzy ...

This paper presents pattern recognition of time series data and subsequent temporal data mining of power signal disturbance events that occur frequently in power distribution networks using multiresolution S-transform and Fuzzy neural inference system . This system yields relevant features, which are used in a Fuzzy expert system to separate the transient time series data and steady state short...

Since its birth in 1993, fuzzy time series have seen different classes of models designed and applied, such as fuzzy logic relation and rule-based models. These models have both advantages and disadvantages. The major drawbacks with these two classes of models are the difficulties encountered in identification and analysis of the model. Therefore, there is a strong need to explore new alternati...

kahman is the most beneficial river in alashtar city for agriculture and aquiculture. as hydrology processes have random nature, statistics and probability are base of analysis of these processes and time series are used for this purpose. the first step in time series analysis includes parameters variation through time. second step is to stationary data, third is normalization and forth is mode...