Abstract—Study of fuzzy time series has increasingly attracted much attention due to its salient capabilities of tackling vague and incomplete data. A variety of forecasting models have devoted to improve forecasting accuracy. Recently, Fuzzy time-series based on Fibonacci sequence has been proposed as a new fuzzy time series model which incorporates the concept of the Fibonacci sequence, the f...

Forecasting the behavior of the financial market is a nontrivial task that relies on the discovery of strong empirical regularities in observations of the system. These regularities are often masked by noise and the financial time series often have nonlinear and non-stationary behavior. With the rise of artificial intelligence technology and the growing interrelated markets of the last two deca...

Stock market represents an essential part of the economy in the Middle East, it is significant for shareholders and investors to estimate the stock price and select the best trading opportunity accurately in advance. This paper utilizes artificial neural network in the modeling of stock market exchange prices. The network was trained using supervised learning. Simulation was conducted for seven...

In recent years, many academy researchers have proposed several forecasting models based on technical analysis to predict models such as Engle (1982) and Cheng, Chen, and Wei (2010). After reviewing the literature, two major drawbacks are found in past models: (1) the forecasting models based on artificial intelligence algorithms (AI), such as neural networks (NN) and genetic algorithms (GAs), ...

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,...

Abstract—The aim of this paper is to select the most accurate forecasting method for predicting the future values of the unemployment rate in selected European countries. In order to do so, several forecasting techniques adequate for forecasting time series with trend component, were selected, namely: double exponential smoothing (also known as Holt`s method) and Holt-Winters` method which acco...

the purpose of resent research is to analysis and compares performance evaluation models of selected investment companies in tehran stock exchange market in the field of their portfolio management. the duration of research was between years 2009-2014. statistical society the research is consisting of all active investment companies in in tehran stock exchange market which were 30 companies. vol...

Stock market prediction is important and of great interest because successful prediction of stock prices may promise attractive benefits. These tasks are highly complicated and very difficult. In this paper, we investigate the predictability of stock market return with Adaptive Network-Based Fuzzy Inference System (ANFIS). The objective of this study is to determine whether an ANFIS algorithm i...

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a “data mining-based evolutionary fuzzy expert system” (DEFE...

this paper investigates the forecasting gold coin futures contract price in iran mercantile exchange. this research has presented a hybrid model based on genetic fuzzy systems (gfs) and artificial neural network (ann) to forecast the gold futures contract, at first, we use stepwise regression analysis (sra) to determine factors which have most influence on stock prices. at the next stage we div...