A comparative study of autoregressive neural network hybrids

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...

Full description

Saved in:
Bibliographic Details
Published in:Neural networks 2005-07, Vol.18 (5), p.781-789
Main Authors: Taskaya-Temizel, Tugba, Casey, Matthew C.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
ARIMA
Artificial intelligence
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Hybrid architectures
Linear Models
Models, Neurological
Neural Networks (Computer)
Regression Analysis
Seasonal time series
Seasons
Time-delay neural networks
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/j.neunet.2005.06.003