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Title: Applications of self-organising fuzzy neural networks in financial time series analysis
Author: McDonald, Scott
ISNI:       0000 0004 5992 4830
Awarding Body: Ulster University
Current Institution: Ulster University
Date of Award: 2016
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The forecasting of financial time series is a major research area in statistics, econometrics and, increasingly, computational intelligence. Financial data are known to be extremely complex, nonstationary, and nonlinear in their composition. Machine learning algorithms have shown themselves to be capable of modelling complex datasets, particularly when compared with traditional statistical models. In particular, artificial neural networks are one of the most popular models in the literature. This thesis explores the usage of a particular type of neural network, namely a self organising fuzzy neural network (SOFNN), for financial forecasting applications. A general overview of the computational methods used in the experimental chapters, as well as a review of the existing literature, are presented in Chapters 2 and 3. Chapter 4 investigates the usage of the SOFNN applied to stock price prediction, using random forests and a multi-objective genetic algorithm to automate input variable and parameter selection. In Chapter 5, the efficacy of combining linear statistical models and nonlinear machine learning models is investigated. The effects of combining the forecasts of various models into groups, or ensembles, are also evaluated. Finally, in Chapter 6, an Interval Type 2 (IT2) SOFNN is designed and implemented. It is a more general form of the networks used in Chapters 4 and 5, ba'3ed on Type 2 fuzzy logic. The accuracy and robustness of the Hew model's forecasts are evaluated using a number of financial time series. The results of this work show that the SOFNN is a suitable choice for forecasting financial data. Its dynamic structure and online learning algorithm are particularly useful when dealing with complex, nonstationary data. However, no single model can be expected to he superior to all others in every situation. It is shown that comhining the forecasts of multiple models, even those trained on t.he same datasets, can improve overall forecasting accuracy. Finally, the suitability of the IT2 SOFNN for predicting stock prices is established. The increased modelling capabilities of the more general Type 2 fuzzy membership functions, as well as an increased robustness to noise, makes it an attractive choice for this application.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available