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

Single Multiplicative Neuron Model Artificial Neural Network with Autoregressive Coefficient for Time Series Modelling

Bas, Eren/0000-0002-0263-8804; Egrioglu, Erol/0000-0003-4301-4149WOS: 000434268000024Single multiplicative neuron model and multilayer perceptron have been commonly used for time series prediction problem. Having a simple structure and features of easily applicable differentiates the single multiplicative neuron model from the multilayer perception. While, multilayer perceptron just as many other artificial neural networks are data-based methods, single multiplicative neuron model has a model structure due to it is composed of a single neuron. Multilayer perceptron can highly compliance with data by changing its architecture, though single multiplicative neuron model, in this respect, is insufficient. In this study, to overcome this problem of single multiplicative neuron model, a new model that its weights and biases are obtained by way of autoregressive equations is proposed. Since the time indexes are considered to determine weights and biases from the autoregressive models, the proposed neural network can be evaluated as a data-based model. To show the performance and capability of the proposed method, various implementations have been executed over some well-known data sets. And the obtained results demonstrate that data-based proposed method has outstanding forecasting performance