Author: Yip, Chi-kin
Title: Time-series forecasting of foreign exchange rates using recurrent neural networks : a comparative study with statistical models
Degree: M.Sc.
Year: 1998
Subject: Foreign exchange rates -- Forecasting -- Mathematical models
Time series analysis
Neural networks (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Department of Applied Mathematics
Pages: 97 leaves : ill. ; 30 cm + diskette
Language: English
Abstract: Time series are a special form of data where past values in the series may influence future values, depending on the presence of underlying deterministic forces. Whilst linear models, such as those based on regression techniques, have been the basis of traditional statistical forecasting models, their drawbacks have led to increased activity in nonlinear modeling. Neural networks are nonlinear models that can be trained to map past and future values of time series, and thereby extract hidden structure and relationships governing the data. Economy is a dynamic system that inherits non-linearity through long term trends, seasonal patterns, cyclical movements, and irregular factors. This dissertation focus on the application of Recurrent Neural Networks (RNN) to predict Foreign Exchange (FX) time series. The RNN module performance is evaluated in terms of Direction matching, MAE, MAPE, and System stability using FX rate data set. This data set includes four currencies against United States Dollar (USD) as indicators that span over four years in daily price. The predictions are combined trading fuzzy decision making module to generate buy-sell-hold recommendations for the entire list of FX rates on a daily basis. Finally, a simple exponential smoothing (SES) model would be compared to RNN prediction. And the results are presented and concluded with a discussion on the ongoing research direction.
Rights: All rights reserved
Access: restricted access

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