Abstract Time Series Forecasting is the prediction of future values of a signal based on the
observed past values. It has various applications in signal processing, especially in the …

Financial forecasting is an area of research which has been attracting a lot of attention
recently from practitioners in the field of artificial intelligence. Apart from the economic …

Machine learning has successful applications in credit risk management, portfolio
management, automatic trading, and fraud detection, to name a few, in the domain of …

Artificial Neural Networks (ANN) have been frequently applied to reduce risks and maximize
the net returns in different types of algorithm trading. Using a real dataset, and aiming to …

The paper studies time series prediction using artificial neural networks. The special
attention is paid to the influence of size of the input vector length. Furthermore, the prediction …

Purpose This paper aims to detect the most profitable, ie optimal turning points (TPs), from
the history of time series using a binary integer programming (BIP) model. TPs prediction …

In this paper we study the applicability of three data mining techniques, viz. backpropagation
neural network (BPNN), support vector regression (SVR) and multivariate adaptive …

The aim of this study was to model and use machine learning techniques to maximize the
chance of a market maker be executed successfully in a stock market, that is, when their bid …

Purpose The algorithmic trading has advanced exponentially and necessitates the
evaluation of intraday stock market forecasting on the grounds that any stock market series …

Hardware computation of large-scale neural networks models is a challenge in terms of
calculation precision, data bandwidth, memory capacity and overall general system …