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 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.


A temporal neuro-fuzzy approach for time-series analysis
Yılmaz (Şişman), Nuran Arzu; Alpaslan, Ferda Nur; Department of Computer Engineering (2003)
The subject of this thesis is to develop a temporal neuro-fuzzy system for fore- casting the future behavior of a multivariate time series data. The system has two components combined by means of a system interface. First, a rule extraction method is designed which is named Fuzzy MAR (Multivari- ate Auto-regression). The method produces the temporal relationships between each of the variables and past values of all variables in the multivariate time series system in the form of fuzzy rules. These rules may ...
A simulation study on the comparison of methods for the analysis of longitudinal count data
İnan, Gül; İlk Dağ, Özlem; Department of Statistics (2009)
The longitudinal feature of measurements and counting process of responses motivate the regression models for longitudinal count data (LCD) to take into account the phenomenons such as within-subject association and overdispersion. One common problem in longitudinal studies is the missing data problem, which adds additional difficulties into the analysis. The missingness can be handled with missing data techniques. However, the amount of missingness in the data and the missingness mechanism that the data ha...
A Computational approach to detect inhomogeneities in time series data
Yazıcı, Ceyda; Yozgatlıgil, Ceylan; Batmaz, İnci; Department of Statistics (2017)
Detection of possible inhomogeneity within a series is an important problem in time series data. There are many sources from which inhomogeneity can be originated such as mean shift, variance and trend change, gradual change, or sudden decrease or increase in time series. Since time series has many application areas, the detection of changepoints should be investigated before conducting any analysis. Available methods have certain drawbacks that may lead to unreliable inferences. These include the need of i...
The effect of temporal aggregation on univariate time series analysis
Sarıaslan, Nazlı; Yozgatlıgil, Ceylan; Department of Statistics (2010)
Most of the time series are constructed by some kind of aggregation and temporal aggregation that can be defined as aggregation over consecutive time periods. Temporal aggregation takes an important role in time series analysis since the choice of time unit clearly influences the type of model and forecast results. A totally different time series model can be fitted on the same variable over different time periods. In this thesis, the effect of temporal aggregation on univariate time series models is studie...
A Shrinkage Approach for Modeling Non-Stationary Relational Autocorrelation
Angın, Pelin (2008-12-19)
Recent research has shown that collective classification in relational data often exhibit significant performance gains over conventional approaches that classify instances individually. This is primarily due to the presence of autocorrelation in relational datasets, meaning that the class labels of related entities are correlated and inferences about one instance can be used to improve inferences about linked instances. Statistical relational learning techniques exploit relational autocorrelation by modeli...
Citation Formats
T. Taşkaya Temizel, “A comparative study of autoregressive neural network hybrids,” NEURAL NETWORKS, pp. 781–789, 2005, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30394.