An investigation into the use of combined linear and neural network models for time series data / A.S. Kruger.

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dc.contributor.author Kruger, Albertus Stephanus
dc.date.accessioned 2011-09-27T13:18:09Z
dc.date.available 2011-09-27T13:18:09Z
dc.date.issued 2009
dc.identifier.uri http://hdl.handle.net/10394/4782
dc.description Thesis (M.Sc. (Computer Science))--North-West University, Vaal Triangle Campus, 2010. en
dc.description.abstract Time series forecasting is an important area of forecasting in which past observations of the same variable are collected and analyzed to develop a model describing the underlying relationship. The model is then used to extrapolate the time series into the future. This modeling approach is particularly useful when little knowledge is available on the underlying data generating process or when there is no satisfactory explanatory model that relates the prediction variable to other explanatory variables. Time series can be modeled in a variety of ways e.g. using exponential smoothing techniques, regression models, autoregressive (AR) techniques, moving averages (MA) etc. Recent research activities in forecasting also suggested that artificial neural networks can be used as an alternative to traditional linear forecasting models. This study will, along the lines of an existing study in the literature, investigate the use of a hybrid approach to time series forecasting using both linear and neural network models. The proposed methodology consists of two basic steps. In the first step, a linear model is used to analyze the linear part of the problem and in the second step a neural network model is developed to model the residuals from the linear model. The results from the neural network can then be used to predict the error terms for the linear model. This means that the combined forecast of the time series will depend on both models. Following an overview of the models, empirical tests on real world data will be performed to determine the forecasting performance of such a hybrid model. Results have indicated that depending on the forecasting period, it might be worthwhile to consider the use of a hybrid model. en_US
dc.publisher North-West University en_US
dc.subject Time series en_US
dc.subject Forecasting en_US
dc.subject Linear models en_US
dc.subject Neural networks en_US
dc.subject Hybrid models en_US
dc.title An investigation into the use of combined linear and neural network models for time series data / A.S. Kruger. en_US
dc.type Thesis en_US
dc.description.thesistype Masters en_US

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