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...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

this paper employs a general non-linear analysis tool to analyse the nature of time series associated with the price (returns) of a particular company in tehran stock exchange. it is shown that the behavior of the process associated with the price (returns) time-series of this company is weakly chaotic, and due to the non-random behavior of the process, short term prediction of stock price is p...

using nonlinear mathematical analysis, on the data obtained for.time seriesof shahd-iran price during 3.5 years, the characteristics,of the process associated with this, is analysed. analysing the behaviour of the time series associated with returns is indicative ofits short-term predictability nature. however, employing analysis regarding the correlation dimension estimate. it is indicated tha...

‎financial market modeling and prediction is a difficult problem and drastic changes of the price causes nonlinear dynamic that makes the price prediction one of the most challenging tasks for economists‎. ‎since markets always have been interesting for traders‎, ‎many traders with various beliefs are highly active in a market‎. ‎the competition among two agents of traders‎, ‎namely trend follo...

This paper employs a general non-linear analysis tool to analyse the nature of time series associated with the price (returns) of a particular company in Tehran Stock Exchange. It is shown that the behavior of the process associated with the price (returns) time-series of this company is weakly chaotic, and due to the non-random behavior of the process, short term prediction of stock price is p...

This paper employs a general non-linear analysis tool to analyse the nature of time series associated with the price (returns) of a particular company in Tehran Stock Exchange. It is shown that the behavior of the process associated with the price (returns) time-series of this company is weakly chaotic, and due to the non-random behavior of the process, short term prediction of stock price is p...

Price prediction is one of the examples related to forecasting tasks and a project based on data science. analyzes predicts cost new products. The goal this research achieve an arrangement predict price cellphone its specifications. So, five deep learning models are proposed range cellphone, unimodal four multimodal approaches. methods prices graphical non-graphical features cellphones that hav...

In the global economy, crude oil is among the most important strategic goods that affects the performance of local and international markets. Prediction of the oil price has always been an important challenging topic in the global economy and producers and consumers have constantly been trying to improve their roll in the oil price changes and for many years OPEC has been one of the key players...

in this paper, a model based on gmdh type neural network, is used to predict gas price in the spot market while using oil spot market price, gas spot market price, gas future market price, oil future market price and average temperature of the weather. the results suggest that gmdh neural network model, according to the root mean squared error (rmse) and direction statistics (dstat) statistics ...