In the past two decades, there has been much interest in applying neural networks to
financial time series forecasting. Yet, there has been relatively little attention paid to …

Most of the existing systems recommend single neural network architecture to be used for a
particular time series. Our experiments have shown that a fixed architecture may not be the …

Predicting the return of financial times series using traditional technical analysis and widely
used economic models such as the capital asset pricing model, has proven to be difficult …

Artificial neural networks are universal and highly flexible function approximators first used
in the fields of cognitive science and engineering. In recent years, neural network …

This chapter presents a combined ARIMA and neural network approach for time series
forecasting. The model contains three steps:(1) fitting a linear ARIMA model to the time …

From the Publisher: A neural network is a computer program that can recognise patterns in
data, learn from this and (in the case of time series data) make forecasts of future patterns …

Artificial neural networks (ANNs) have received more and more attention in time series
forecasting in recent years. One major disadvantage of neural networks is that there is no …

This paper investigates non‐stationary time series analysis and forecasting techniques for
financial datasets. We focus on the use of a popular non‐stationary parametric model …

Properly comprehending and modeling the dynamics of financial data has indispensable
practical importance. The prime goal of a financial time series model is to provide reliable …

This thesis investigates the application of artificial neural networks (ANNs) for forecasting
financial time series (eg stock prices). The theory of technical analysis dictates that there are …