Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods

Two step feature selection: Approximate functional dependency approach using membership values

Feature selection is one of the most important issues in fields such as system modelling and pattern recognition. In this study, a new feature selection algorithm that combines feature wrapper and feature filter approaches is proposed in order to identify the significant input variables in systems with continuous domains. The proposed method utilizes functional dependency concept and K-Nearest Neighbourhood method to implement the feature filter and feature wrapper, respectively. It is common to have outliers and noise in real-life data. In order to make the proposed feature selection algorithm noise and outlier resistant, approximate functional dependencies are used by utilizing membership values that inherently cope with uncertainty in the data

Rule-by-rule input significance analysis in fuzzy system modeling

Input or feature selection is one the most important steps of system modeling. Elimination of irrelevant variables can save time, money and can improve the precision of model that we are trying to discover. In Fuzzy System Modeling (FSM) approaches, input selection plays an important role too. The input selection algorithms that are under our investigation did not consider one crucial fact. An input variable may of may not be significant in a specific rule, not in overall system. In this paper, an input selection algorithm that takes this observation into account is proposed as an extension of the input selection algorithms found in the literature. The proposed algorithm is applied on a nonlinear function and successful results are achieved

A comparative analysis of fuzzy system modelling approaches: A case in mining medical diagnostic rules

Fuzzy system modeling approximates highly nonlinear systems by means of fuzzy if-then rules. In the literature, different approaches are proposed for mining fuzzy if-then rules from historical data. These approaches usually utilize fuzzy clustering in structure identification phase. In this research, we are going to analyze three possible approaches from the literature and try to compare their performances in a medical diagnosis classification problem, namely Aachen Aphasia Test. Given the fact that the comparison is conducted on a single data set; the conclusions are by no means inclusive. However, we believe that the results might provide some valuable insights

A new fuzzy inference approach based on mamdani inference using discrete type 2 fuzzy sets

Fuzzy System Modeling (FSM) is one of the most prominent system modeling tools in analyzing the data in the presence of uncertainty. Linguistic Fuzzy Rulebase (LFR) structure, in which both the antecedent and consequent variables are represented by fuzzy sets, is the most well known fuzzy rulebase structure in the literature. The proposed FSM method identifies LFR system model by executing Fuzzy C-Means (FCM) clustering method. One of the sources of uncertainty in system modeling is the uncertainty in selecting learning parameters. In order to capture this uncertainty in a more realistic way, the antecedent and consequent variables are represented by using Type 2 fuzzy sets that are constructed by executing FCM method with different level of fuzziness, in, values. The proposed system modeling approach is applied on a well-known benchmark data set where the goal is to predict the price of a stock. After comparing the results with the ones obtained with other system modeling tools, it can be claimed successful results are achieved