In this paper the problem of estimating forecasts, for the Official Market of future contracts for olive oil in Spain, is addressed. Time series analysis and their applications is an emerging research line in the Intelligent Systems field. Among the reasons for carry out time series analysis and forecasting, the associated increment in the benefits of the implied organizations must be highlight...

An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert’s knowledge, it calls a data mining function, a genetic algorithm,...

During recent years, examining the performance of contrarian strategies has been taken into consideration by scholars and stock market's activists, though several evidences are available based on profitability of this type of strategies, much of them have been referred to American and European's stock markets. In current study, the act of these strategies will be examined in Tehran's stock exch...

During recent years, examining the performance of contrarian strategies has been taken into consideration by scholars and stock market's activists, though several evidences are available based on profitability of this type of strategies, much of them have been referred to American and European's stock markets. In current study, the act of these strategies will be examined in Tehran's stock exch...

  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...

In essence, data mining consists of extracting knowledge from data. This paper proposes an evolutionary system for discovering fuzzy classi cation rules. Fuzzy logic is useful for data mining especially in the case for performing classi cation task. Three methods were used to extract fuzzy classi cation rules using Evolutionary Algorithms: (1) genetic selection small number of large number of f...

A fuzzy logic based expert system has been developed that automatically allocates electronic attack (EA) resources in real-time over many dissimilar platforms. The platforms and their foes can be very general, e.g., ships, planes, robots, land based facilities, etc. Genetic algorithm based data mining techniques are described that relate to the development of the resource manager (RM). The use ...

The concept of intelligently controlling the search process of gravitational search algorithm (GSA) is introduced to develop a novel data mining technique. The proposed method is called fuzzy GSA miner (FGSA-miner). At first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major ro...

In this paper, we develop a multilayered genetic based fuzzy image filter, which consists of fuzzy number construction process, a fuzzy filtering process, a genetic learning process and an image knowledge base. The introduction of multilayered fuzzy systems substantially decreases the no of rules to be learnt. First, the fuzzy number construction process receives noise free image and sample ima...

Investment strategies as rules for buy and sell are introduced as conditional statements involving inequalities of various moving averages. Different conditional statements on moving averages are represented as strings, encodable as chromosomes in an approach based on genetic algorithm. The data mining of good investment strategies corresponds to the extraction of rules that are fit in the sens...