A Hybrid Artificial Neural Network Model for Forecasting Short Time Series
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 performa...
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| Format: | Dissertation (University of Nottingham only) |
| Language: | English |
| Published: |
2012
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| Online Access: | https://eprints.nottingham.ac.uk/25828/ |
| Summary: | 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 |
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