A temporal neuro-fuzzy approach for time-series analysis

Yılmaz (Şişman), Nuran Arzu
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 constitute the rule-base in a fuzzy expert system. Second, a temporal neuro-fuzzy system which is named ANFIS unfolded in - time is designed in order to make the use of fuzzy rules, to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data. The rule base of ANFIS unfolded in time contains temporal TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, Back-propagation learning algorithm is used. The system takes the multivariate data and the num- ber of lags needed which are the output of Fuzzy MAR in order to describe a variable and predicts the future behavior. Computer simulations are performed by using synthetic and real multivariate data and a benchmark problem (Gas Furnace Data) used in comparing neuro- fuzzy systems. The tests are performed in order to show how the system efficiently model and forecast the multivariate temporal data. Experimental results show that the proposed model achieves online learning and prediction on temporal data. The results are compared by other neuro-fuzzy systems, specifically ANFIS.


A neuro-fuzzy MAR algorithm for temporal rule-based systems
Sisman, NA; Alpaslan, Ferda Nur; Akman, V (1999-08-04)
This paper introduces a new neuro-fuzzy model for constructing a knowledge base of temporal fuzzy rules obtained by the Multivariate Autoregressive (MAR) algorithm. The model described contains two main parts, one for fuzzy-rule extraction and one for the storage of extracted rules. The fuzzy rules are obtained from time series data using the MAR algorithm. Time-series analysis basically deals with tabular data. It interprets the data obtained for making inferences about future behavior of the variables. Fu...
A temporal neuro-fuzzy approach for time-series analysis
Şişman Yılmaz, Arzu; Alpaslan, Ferda Nur (null; 2003-09-08)
In this paper, a temporal neuro-fuzzy system is presented which provides an environment that keeps temporal rela tionships between input and output variables. The sys tem is used to forecast the future behavior of time series data. It is based on ANFIS neuro-fuzzy system and named ANFIS unfolded in time. The rule base contains tempo ral TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, a modified back-propagation learning algorithm is used. The model is tested on Gas-furnace data which is a benchm...
A temporal neurofuzzy model for rule-based systems
Alpaslan, Ferda Nur; Jain, L (1997-05-23)
This paper reports the development of a temporal neuro-fuzzy model using fuzzy reasoning which is capable of representing the temporal information. The system is implemented as a feedforward multilayer neural network. The learning algorithm is a modification of the backpropagation algorithm. The system is aimed to be used in medical diagnosis systems.
A temporal neural network model for constructing connectionist expert system knowledge bases
Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01)
This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.
Hybrid wavelet-neural network models for time series data
Kılıç, Deniz Kenan; Uğur, Ömür; Department of Financial Mathematics (2021-3-3)
The thesis aims to combine wavelet theory with nonlinear models, particularly neural networks, to find an appropriate time series model structure. Data like financial time series are nonstationary, noisy, and chaotic. Therefore using wavelet analysis helps better modeling in the sense of both frequency and time. S&P500 (∧GSPC) and NASDAQ (∧ IXIC) data are divided into several components by using multiresolution analysis (MRA). Subsequently, each part is modeled by using a suitable neural network structure. ...
Citation Formats
N. A. Yılmaz (Şişman), “A temporal neuro-fuzzy approach for time-series analysis,” Ph.D. - Doctoral Program, Middle East Technical University, 2003.