A Hybrid Artificial Neural Network Model for Forecasting Short Time Series

Mohan, Anil (2012) A Hybrid Artificial Neural Network Model for Forecasting Short Time Series. [Dissertation (University of Nottingham only)] (Unpublished)

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Abstract

Forecasting has long been the domain of traditional statistical models. Recent research has

shown that novel and complex forecasting models do not necessarily outperform simpler models.

These include in particular Artificial Neural Networks (ANNs). Even though claims of superior

forecasting performance were made by Neural Network researchers, these claims were often

unsubstantiated.

Artificial neural networks are information processing paradigms motivated by the information

processing functions of the human brain. ANNs are widely recognized as universal function

approximators and are capable of exploiting nonlinear relationships between variables. Given

these strengths, we believed it was possible to design a neural network that would provide

excellent forecasting ability over a wide variety of data. Inspired by recent research into deep

learning nets, we were able to model a new Hybrid ANN model and compared its performance to

other forecasting models used in the M3 Time Series Competition. The results show that on

average the Hybrid model outperforms the other methods investigated and

Item Type: Dissertation (University of Nottingham only)
Depositing User: EP, Services
Date Deposited: 05 Apr 2013 13:31
Last Modified: 22 Oct 2016 22:27
URI: http://eprints.nottingham.ac.uk/id/eprint/25828

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