Time series forecasting using neural networks
This work addresses time series forecasting for financial data, specifically exchange rates, but is incremental as it applies existing neural network methods to new data without introducing novel techniques.
The paper tackled the problem of forecasting exchange rates (EUR/RON and USD/RON) using neural networks, comparing feed-forward and recurrent architectures with various training algorithms on daily data from 2005 to 2013, and found that neural networks can approximate nonlinear functions effectively for this task.
Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function. Neural networks have been successfully used for forecasting of financial data series. The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. Neural Networks have the advantage that can approximate nonlinear functions. In this paper we compared the performances of different feed forward and recurrent neural networks and training algorithms for predicting the exchange rate EUR/RON and USD/RON. We used data series with daily exchange rates starting from 2005 until 2013.