since the pioneering work of zadeh, fuzzy set theory has been applied to amyriad of areas. song and chissom introduced the concept of fuzzy time series andapplied some methods to the enrolments of the university of alabama. thereafter weapply fuzzy techniques for system identification and apply statistical techniques tomodelling system. an automatic methodology framework that combines fuzzytech...

In the literature, there are few studies analyzing impacts of foreign direct investment and portfolio investments on house prices. We employed quarterly data for 20 EU countries over 2007-2013 period in order to examine separate prices post-financial crisis. The results Panel Vector Auto Regression indicate that negatively affects prices, positively Also, increase lead decrease both investment....

Parameter estimation of the spatial auto-regression model (SAR) is important because we can model the spatial dependency, i.e., spatial autocorrelation present in the geo-spatial data. SAR is a popular data mining technique used in many geo-spatial application domains such as regional economics, ecology, environmental management, public safety, public health, transportation, and business. Howev...

the main objective of this study was to evaluate the response of consumers to changes in income and prices of chicken meat in the short run and long run. in order to achieve this goal, vector auto regression (var) and vector error correction model (vecm) have been applied to data since 1974 to 2007. impulse response function (irf) calculation indicated that past and present consumption of chick...

When an employee is hired by a company, given his/her job role, model can be built to predict an employee’s access needs. This auto-access model tries to minimize the human labor and time for granting or revoking employee access. The input to our algorithm is information about the employee’s role at the time of approval. We then use Logistic Regression, Naive Bayes and Support Vector Machine (S...

abstract nowadays, due to the environmental uncertainty and rapid development of new technologies, economic variables are often predicted by using less data and short-term timeframes. therefore, prediction methods which require fewer amounts of data are needed. auto regressive integrated moving average (arima) model and artificial neural networks (anns) need large amounts of data to achieve acc...