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

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

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.

scholarly journals A Novel Ensemble Neuro-Fuzzy Model for Financial Time Series Forecasting

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 126 ◽  
Author(s):  
Vlasenko ◽  
Vlasenko ◽  
Vynokurova ◽  
Bodyanskiy ◽  
Peleshko
Keyword(s):  
Decision Making ◽  
Time Series ◽  
Financial Time Series ◽  
Selection Process ◽  
Real Life ◽  
Fuzzy Model ◽  
Financial Data ◽  
Financial Time ◽  
Fuzzy Models ◽  
Neuro Fuzzy

Neuro-fuzzy models have a proven record of successful application in finance. Forecasting future values is a crucial element of successful decision making in trading. In this paper, a novel ensemble neuro-fuzzy model is proposed to overcome limitations and improve the previously successfully applied a five-layer multidimensional Gaussian neuro-fuzzy model and its learning. The proposed solution allows skipping the error-prone hyperparameters selection process and shows better accuracy results in real life financial data.

scholarly journals Intermittent stream flow forecasting and modelling with hybrid wavelet neuro-fuzzy model

2017 ◽  
Vol 49 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Honey Badrzadeh ◽  
Ranjan Sarukkalige ◽  
A. W. Jayawardena
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Stream Flow ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Performance Criteria ◽  
Discrete Wavelet ◽  
Inference System ◽  
Input Selection ◽  
Neuro Fuzzy

Abstract In this paper, an advanced stream flow forecasting model is developed by applying data-preprocessing techniques on adaptive neuro-fuzzy inference system (ANFIS). Wavelet multi-resolution analysis is coupled with an ANFIS model to develop a hybrid wavelet neuro-fuzzy (WNF) model. Different models with different input selection and structures are developed for daily, weekly and monthly stream flow forecasting in Railway Parade station on Ellen Brook River, Western Australia. The stream flow time series is decomposed into multi-frequency time series by discrete wavelet transform using the Haar, Coiflet and Daubechies mother wavelets. The wavelet coefficients are then imposed as input data to the neuro-fuzzy model. Models are developed based on Takagi-Sugeno-Kang fuzzy inference system with the grid partitioning approach for initializing the fuzzy rule-based structure. Mean-square error and Nash-Sutcliffe coefficient are chosen as the performance criteria. The results of the application show that the right selection of the inputs with high autocorrelation function improves the accuracy of forecasting. Comparing the performance of the hybrid WNF models with those of the original ANFIS models indicates that the hybrid WNF models produce significantly better results especially in longer-term forecasting.

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