This paper explores the conceptual background to financial time series analysis and financial signal processing in terms of the Efficient Market Hypothesis. By revisiting the principal conventional approaches to market analysis and the reasoning associated with them, we develop a Fractal Market Hypothesis that is based on the application of non-stationary fractional dynamics using an operator o...

As the number of publicly traded companies as well as the amount of their financial data grows rapidly and improvements in hardware infrastructure and information processing technologies enable high-speed processing of large amounts of data, it is highly desired to have tracking, analysis, and eventually stock selections automated. Machine learning has already attained an important place in tra...

objective: international diabetes organization estimates that there are 285 million people worldwide who suffer from diabetes, and this figure is expected to increase to 450 million in next 20 years. according to statistics issued by the world health organization, diabetes is considered among ten leading causes of death in world and its prevalence in the population is growing.this paper deals w...

A data stream is a real-time, continuous, ordered sequence of items generated by sources such as sensor networks, Internet traffic flow, credit card transaction logs, and on-line financial tickers. Processing continuous queries over data streams introduces a number of research problems, one of which concerns evaluating queries over sliding windows defined on the inputs. In this paper, we descri...

Financial forecasting is an example of signal processing problems. A number of ways to train/learn the network are available. We have used Levenberg-Marquardt algorithm for error back-propagation for weight adjustment. Pre-processing of data has reduced much of the variation at large scale to small scale, reducing the variation of training data. Keywords— Gradient descent method, jacobian matri...

Financial forecasting is an example of signal processing problems. A number of ways to train/learn the network are available. We have used Levenberg-Marquardt algorithm for error back-propagation for weight adjustment. Pre-processing of data has reduced much of the variation at large scale to small scale, reducing the variation of training data. Keywords— Gradient descent method, jacobian matri...