Fast evolving neuro-fuzzy model and its application in online classification and time series prediction

2011 ◽  
Vol 15 (3) ◽  
pp. 279-288 ◽  
Author(s):  
Hossein Soleimani-B. ◽  
Caro Lucas ◽  
Babak N. Araabi
Keyword(s):  
Time Series ◽  
Time Series Prediction ◽  
Fuzzy Model ◽  
Online Classification ◽  
Neuro Fuzzy

scholarly journals A Novel Neuro-Fuzzy Model for Multivariate Time-Series Prediction

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 62 ◽  
Author(s):  
Alexander Vlasenko ◽  
Nataliia Vlasenko ◽  
Olena Vynokurova ◽  
Dmytro Peleshko
Keyword(s):  
Time Series ◽  
Learning Algorithm ◽  
Time Series Prediction ◽  
Multivariate Time Series ◽  
Fuzzy Model ◽  
Nonlinear Behavior ◽  
Additional Information ◽  
Computational Performance ◽  
Neuro Fuzzy ◽  
Competing Models

Time series forecasting can be a complicated problem when the underlying process shows high degree of complex nonlinear behavior. In some domains, such as financial data, processing related time-series jointly can have significant benefits. This paper proposes a novel multivariate hybrid neuro-fuzzy model for forecasting tasks, which is based on and generalizes the neuro-fuzzy model with consequent layer multi-variable Gaussian units and its learning algorithm. The model is distinguished by a separate consequent block for each output, which is tuned with respect to the its output error only, but benefits from extracting additional information by processing the whole input vector including lag values of other variables. Numerical experiments show better accuracy and computational performance results than competing models and separate neuro-fuzzy models for each output, and thus an ability to implicitly handle complex cross correlation dependencies between variables.

Simulation of time-series groundwater parameters using a hybrid metaheuristic neuro-fuzzy model

2022 ◽  
Author(s):  
Ali Azizpour ◽  
Mohammad Ali Izadbakhsh ◽  
Saeid Shabanlou ◽  
Fariborz Yosefvand ◽  
Ahmad Rajabi
Keyword(s):  
Time Series ◽  
Fuzzy Model ◽  
Hybrid Metaheuristic ◽  
Neuro Fuzzy

ANNEALED CHAOTIC LEARNING FOR TIME SERIES PREDICTION IN IMPROVED NEURO-FUZZY NETWORK WITH FEEDBACKS

2009 ◽  
Vol 08 (04) ◽  
pp. 429-444 ◽  
Author(s):  
CATHERINE VAIRAPPAN ◽  
SHANGCE GAO ◽  
ZHENG TANG ◽  
HIROKI TAMURA
Keyword(s):  
Time Series ◽  
Fuzzy System ◽  
Chaotic Dynamics ◽  
Time Series Prediction ◽  
Data Series ◽  
Temporal Data ◽  
Optimal Solutions ◽  
Benchmark Data ◽  
Neuro Fuzzy ◽  
Fuzzy Network

A new version of neuro-fuzzy system of feedbacks with chaotic dynamics is proposed in this work. Unlike the conventional neuro-fuzzy, improved neuro-fuzzy system with feedbacks is better able to handle temporal data series. By introducing chaotic dynamics into the feedback neuro-fuzzy system, the system has richer and more flexible dynamics to search for near-optimal solutions. In the experimental results, performance and effectiveness of the presented approach are evaluated by using benchmark data series. Comparison with other existing methods shows the proposed method for the neuro-fuzzy feedback is able to predict the time series accurately.

Tourism Demand Forecasting Based on a Neuro-Fuzzy Model

2014 ◽  
Vol 1 (1) ◽  
pp. 60-69 ◽  
Author(s):  
George Atsalakis ◽  
Eleni Chnarogiannaki ◽  
Consantinos Zopounidis
Keyword(s):  
Time Series ◽  
Fuzzy Inference ◽  
Demand Forecasting ◽  
Fuzzy Model ◽  
Time Series Models ◽  
Tourism Demand ◽  
Anfis Model ◽  
Inference System ◽  
Forecasting Models ◽  
Neuro Fuzzy

Tourism in Greece plays a major role in the country's economy and an accurate forecasting model for tourism demand is a useful tool, which could affect decision making and planning for the future. This paper answers some questions such as: how did the forecasting techniques evolve over the years, how precise can they be, and in what way can they be used in assessing the demand for tourism? An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been used in making the forecasts. The data used as input for the forecasting models relates to monthly time-series tourist arrivals by air, train, sea and road into Greece from January 1996 until September 2011. 80% of the data has been used to train the forecasting models and the rest to evaluate the models. The performance of the model is achieved by the calculation of some well known statistical errors. The accuracy of the ANFIS model is further compared with two conventional forecasting models: the autoregressive (AR) and autoregressive moving average (ARMA) time-series models. The results were satisfactory even if the collected data were not pleasing enough. The ANFIS performed further compared to the other time-series models. In conclusion, the accuracy of the ANFIS model forecast proved its great importance in tourism demand forecasting.

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