A comparative study of autoregressive neural network hybrids
Taskaya-Tamizel, T and Casey, Matthew C (2005) A comparative study of autoregressive neural network hybrids Neural Networks, 18 (5-6). pp. 781-789.
Many researchers have argued that combining many models for forecasting gives better estimates than single time series models. For example, a hybrid architecture comprising an autoregressive integrated moving average model (ARIMA)and a neural network is a well-known technique that has recently been shown to give better forecasts by taking advantage of each model’s capabilities. However, this assumption carries the danger of underestimating the relationship between the model’s linear and non-linear components, particularly by assuming that individual forecasting techniques are appropriate, say, for modeling the residuals. In this paper, we show that such combinations do not necessarily outperform individual forecasts. On the contrary, we show that the combined forecast can underperform significantly compared to its constituents’ performances. We demonstrate this using nine data sets, autoregressive linear and time-delay neural network models.
|Divisions :||Faculty of Engineering and Physical Sciences > Computing Science|
|Date :||1 August 2005|
|Identification Number :||https://doi.org/10.1016/j.neunet.2005.06.003|
|Uncontrolled Keywords :||Hybrid architectures, seasonal time series, time-delay neural networks, ARIMA|
|Additional Information :||This is an author-prepared version of an article published in Neural Networks, 18, 781-789. © 2005 Elsevier Inc. All rights reserved. Click here to access the publisher's version.|
|Depositing User :||Mr Adam Field|
|Date Deposited :||27 May 2010 14:09|
|Last Modified :||05 Dec 2016 13:55|
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