Generally, trend detection algorithms over the data stream require expert assistance in some form. We present an unsupervised multiscale data stream algorithm which detects trends for evolving time series based on a data driver data stream. The raw stream data clustering algorithm is incremental, space dilating and has linear time complexity. The evolving stream is incrementally explored on a n...

Efficient Market Hypothesis is the popular theory about stock prediction. With its failure much research has been carried in the area of prediction of stocks. This project is about taking non quantifiable data such as financial news articles about a company and predicting its future stock trend with news sentiment classification. Assuming that news articles have impact on stock market, this is ...

Prediction of stock market trends has been an area of great interest both to researchers attempting to uncover the information hidden in the stock market data and for those who wish to profit by trading stocks. The extremely nonlinear nature of the stock market data makes it very difficult to design a system that can predict the future direction of the stock market with sufficient accuracy. Thi...

Recently, many researches attempt to apply data mining methods to construct attractive decision support models for stock prediction. These models mainly focus on forecasting the price trend and providing advice for investors. According to the practical requirements, this paper proposes a model based on the combination of financial indicators and data mining methods to help fund managers make de...

This paper presents performance analysis of hybrid model comprise of concordance and Genetic Programming (GP) to forecast financial market with some existing models. This scheme can be used for in depth analysis of stock market. Different measures of concordances such as Kendall’s Tau, Gini’s Mean Difference, Spearman’s Rho, and weak interpretation of concordance are used to search for the patt...

We design a procedure for measuring the United States capital stock of money implied by the Divisia monetary aggregate service flow, in a manner consistent with the present-value model of economic capital stock. We permit non-martingale expectations and time varying discount rates. Based on Barnett’s (1991) definition of the economic stock of money, we compute the U.S. economic stock of money b...

The daily fluctuation trends of a stock market are illustrated by three statuses: up, equal, and down. These can be represented by a neutrosophic set which consists of three functions—truth-membership, indeterminacy-membership, and falsity-membership. In this paper, we propose a novel forecasting model based on neutrosophic set theory and the fuzzy logical relationships between the status of hi...

This study presents an integrated system where wavelet transforms and recurrent neural network (RNN) based on artificial bee colony (abc) algorithm (called ABC-RNN) are combined for stock price forecasting. The system comprises three stages. First, the wavelet transform using the Harr wavelet is applied to decompose the stock price time series and thus eliminate noise. Second, the RNN, which ha...

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...