Tourism Demand Forecasting Based on a Neuro-Fuzzy Model

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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Novel Hybrid Data-Intelligence Model for Forecasting Monthly Rainfall with Uncertainty Analysis

Water ◽

10.3390/w11030502 ◽

2019 ◽

Vol 11 (3) ◽

pp. 502 ◽

Cited By ~ 30

Author(s):

Zaher Yaseen ◽

Isa Ebtehaj ◽

Sungwon Kim ◽

Hadi Sanikhani ◽

H. Asadi ◽

...

Keyword(s):

Uncertainty Analysis ◽

Fuzzy Inference ◽

Monthly Rainfall ◽

Rainfall Time Series ◽

Anfis Model ◽

Inference System ◽

Forecasting Models ◽

Neuro Fuzzy ◽

Hybrid Data ◽

Better Than

In this research, three different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution (DE), are integrated with the adaptive neuro-fuzzy inference system (ANFIS) model. The developed hybrid models are proposed to forecast rainfall time series. The capability of the proposed evolutionary hybrid ANFIS was compared with the conventional ANFIS in forecasting monthly rainfall for the Pahang watershed, Malaysia. To select the optimal model, sixteen different combinations of six different lag attributes taking into account the effect of monthly, seasonal, and annual history were considered. The performances of the forecasting models were assessed using various forecasting skill indicators. Moreover, an uncertainty analysis of the developed forecasting models was performed to evaluate the ability of the hybrid ANFIS models. The bound width of 95% confidence interval (d-factor) and the percentage of observed samples which was enveloped by 95% forecasted uncertainties (95PPU) were used for this purpose. The results indicated that all the hybrid ANFIS models performed better than the conventional ANFIS and for all input combinations. The obtained results showed that the models with best input combinations had the (95PPU and d-factor) values of (91.67 and 1.41), (91.03 and 1.41), (89.74 and 1.42), and (88.46 and 1.43) for ANFIS-PSO, ANFIS-GA, ANFIS-DE, and the conventional ANFIS, respectively. Based on the 95PPU and d-factor, it is concluded that all hybrid ANFIS models have an acceptable degree of uncertainty in forecasting monthly rainfall. The results of this study proved that the hybrid ANFIS with an evolutionary algorithm is a reliable modeling technique for forecasting monthly rainfall.

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Tourism Demand Forecasting Using Stacking Ensemble Model With Adaptive Fuzzy Combiner

10.21203/rs.3.rs-734279/v1 ◽

2021 ◽

Author(s):

Selcuk Cankurt ◽

Abdulhamit Subasi

Keyword(s):

Neural Networks ◽

Demand Forecasting ◽

Fuzzy Model ◽

Ensemble Model ◽

Tourism Demand ◽

Neural Network Models ◽

Inference System ◽

Neuro Fuzzy ◽

Soft Computing Techniques ◽

Ann Models

Abstract Over the last decades, several soft computing techniques have been applied to tourism demand forecasting. Among these techniques, a neuro-fuzzy model, Adaptive neuro fuzzy inference system (ANFIS) has started to emerge. A conventional ANFIS model cannot deal with the large dimension of a dataset, and cannot work with our dataset, which is a composed of a 62 time-series, as well. This study attempts to develop an ensemble model by incorporating neural networks with ANFIS to deal with the large number of input variables for multivariate forecasting. Our proposed approach is a collaboration of two base learners, which are types of the neural network models and a meta-learner of ANFIS in the framework of the stacking ensemble. The results show that the stacking ensemble of ANFIS (meta-learner) and ANN models (base learners) outperforms its stand-alone counterparts of base learners. Numerical results indicate that the proposed ensemble model achieved a MAPE of 7.26% compared to its single-instance ANN models with MAPEs of 8.50% and 9.18% respectively. Finally, this study which is a novel application of the ensemble systems in the context of tourism demand forecasting has shown better results compared to those of the single expert systems based on the artificial neural networks.

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A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting

Electronics ◽

10.3390/electronics8101071 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1071 ◽

Cited By ~ 8

Author(s):

Mohammed A. A. Al-qaness ◽

Mohamed Abd Elaziz ◽

Ahmed A. Ewees ◽

Xiaohui Cui

Keyword(s):

Time Series ◽

Fuzzy Inference System ◽

Fuzzy Inference ◽

Time Series Prediction ◽

Prediction Method ◽

Oil Consumption ◽

Anfis Model ◽

Inference System ◽

Neuro Fuzzy ◽

Prediction Problems

Oil is the primary source of energy, therefore, oil consumption forecasting is essential for the necessary economic and social plans. This paper presents an alternative time series prediction method for oil consumption based on a modified Adaptive Neuro-Fuzzy Inference System (ANFIS) model using the Multi-verse Optimizer algorithm (MVO). MVO is applied to find the optimal parameters of the ANFIS. Then, the hybrid method, namely MVO-ANFIS, is employed to forecast oil consumption. To evaluate the performance of the MVO-ANFIS model, a dataset of two different countries was used and compared with several forecasting models. The evaluation results show the superiority of the MVO-ANFIS model over other models. Moreover, the proposed method constitutes an accurate tool that effectively improved the solution of time series prediction problems.

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A GA-Weighted Adaptive Neuro-Fuzzy Model to Predict the Behaviour of Magnetorheological Damper

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.663.203 ◽

2014 ◽

Vol 663 ◽

pp. 203-207 ◽

Cited By ~ 2

Author(s):

Mohammadjavad Zeinali ◽

Saiful Amri Mazlan ◽

Abdul Yasser Abd Fatah ◽

Hairi Zamzuri

Keyword(s):

Neural Network ◽

Fuzzy Inference ◽

Fuzzy Model ◽

Prediction Performance ◽

Magnetorheological Damper ◽

Anfis Model ◽

Inference System ◽

Neuro Fuzzy ◽

Ga Algorithm ◽

Artificial Neural Network Ann

Magnetorheological damper is a controllable device in semi-active suspension system to absorb unwanted movement. The accuracy of magnetorheological damper model will affect performance of the control system. In this paper, a combination of genetic algorithm (GA) and adaptive-network-based fuzzy inference system (ANFIS) approaches is utilized to model the magnetorheological damper using experimental results. GA algorithm is implemented to modify the weights of the trained ANFIS model. The proposed method is compared with ANFIS and artificial neural network (ANN) methods to evaluate the prediction performance. The result illustrates that the proposed GA-weighted adaptive neuro-fuzzy model has successfully predicted the magnetorheological damper behaviour and outperformed other compared methods.

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Application of particle swarm optimization with ANFIS model for double scroll chaotic system

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i1.pp328-335 ◽

2021 ◽

Vol 11 (1) ◽

pp. 328

Author(s):

W. A. Wali

Keyword(s):

Time Series ◽

Fuzzy Inference ◽

Time Series Prediction ◽

Particle Swarm ◽

Anfis Model ◽

Inference System ◽

Nonlinear Resistor ◽

Additional Information ◽

Neuro Fuzzy ◽

Chaotic Time Series Prediction

The predictions for the original chaos patterns can be used to correct the distorted chaos pattern which has changed due to any changes whether from undesired disturbance or additional information which can hide under chaos pattern. This information can be recovered when the original chaos pattern is predicted. But unpredictability is most features of chaos, and time series prediction can be used based on the collection of past observations of a variable and analysis it to obtain the underlying relationships and then extrapolate future time series. The additional information often prunes away by several techniques. This paper shows how the chaotic time series prediction is difficult and distort even if Neuro-Fuzzy such as Adaptive Neural Fuzzy Inference System (ANFIS) is used under any disturbance. The paper combined particle swarm (PSO) and (ANFIS) to exam the prediction model and predict the original chaos patterns which comes from the double scroll circuit. Changes in the bias of the nonlinear resistor were used as a disturbance. The predicted chaotic data is compared with data from the chaotic circuit.

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

Hydrology Research ◽

10.2166/nh.2017.163 ◽

2017 ◽

Vol 49 (1) ◽

pp. 27-40 ◽

Cited By ~ 11

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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Tourism forecasting: A review of methodological developments over the last decade

Tourism Economics ◽

10.1177/1354816618812588 ◽

2018 ◽

Vol 25 (3) ◽

pp. 469-492 ◽

Cited By ~ 22

Author(s):

Eden Xiaoying Jiao ◽

Jason Li Chen

Keyword(s):

Time Series ◽

Spatial Regression ◽

Demand Forecasting ◽

Time Series Models ◽

Tourism Demand ◽

Mixed Frequency ◽

Forecasting Models ◽

The Past ◽

Forecasting Performance ◽

Performance Evaluation And Comparison

This study reviewed 72 studies in tourism demand forecasting during the period from 2008 to 2017. Forecasting models are reviewed in three categories: econometric, time series and artificial intelligence (AI) models. Econometric and time series models that have already been widely used before 2007 remained their popularity and were more often used as benchmark models for forecasting performance evaluation and comparison with respect to new models. AI models are rapidly developed in the past decade and hybrid AI models are becoming a new trend. And some new trends with regard to the three categories of models have been identified, including mixed frequency, spatial regression and combination and hybrid models. Different combination components and combination techniques have been discussed. Results in different studies proved superiority of combination forecasts over average single forecasts performance.

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Tourism demand forecasting using stacking ensemble model with adaptive fuzzy combiner

Soft Computing ◽

10.1007/s00500-021-06695-0 ◽

2022 ◽

Author(s):

Selcuk Cankurt ◽

Abdulhamit Subasi

Keyword(s):

Neural Networks ◽

Demand Forecasting ◽

Fuzzy Model ◽

Ensemble Model ◽

Tourism Demand ◽

Neural Network Models ◽

Inference System ◽

Neuro Fuzzy ◽

Soft Computing Techniques ◽

Ann Models

AbstractOver the last decades, several soft computing techniques have been applied to tourism demand forecasting. Among these techniques, a neuro-fuzzy model of ANFIS (adaptive neuro-fuzzy inference system) has started to emerge. A conventional ANFIS model cannot deal with the large dimension of a dataset, and cannot work with our dataset, which is composed of a 62 time-series, as well. This study attempts to develop an ensemble model by incorporating neural networks with ANFIS to deal with a large number of input variables for multivariate forecasting. Our proposed approach is a collaboration of two base learners, which are types of the neural network models and a meta-learner of ANFIS in the framework of the stacking ensemble. The results show that the stacking ensemble of ANFIS (meta-learner) and ANN models (base learners) outperforms its stand-alone counterparts of base learners. Numerical results indicate that the proposed ensemble model achieved a MAPE of 7.26% compared to its single-instance ANN models with MAPEs of 8.50 and 9.18%, respectively. Finally, this study which is a novel application of the ensemble systems in the context of tourism demand forecasting has shown better results compared to those of the single expert systems based on the artificial neural networks.

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Optimized ANFIS Model Using Aquila Optimizer for Oil Production Forecasting

Processes ◽

10.3390/pr9071194 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1194

Author(s):

Ayman Mutahar AlRassas ◽

Mohammed A. A. Al-qaness ◽

Ahmed A. Ewees ◽

Shaoran Ren ◽

Mohamed Abd Abd Elaziz ◽

...

Keyword(s):

Time Series ◽

Optimization Algorithm ◽

Fuzzy Inference ◽

Oil Production ◽

Anfis Model ◽

Inference System ◽

Production Forecasting ◽

Neuro Fuzzy ◽

Hybrid Intelligence ◽

Real World Datasets

Oil production forecasting is one of the essential processes for organizations and governments to make necessary economic plans. This paper proposes a novel hybrid intelligence time series model to forecast oil production from two different oil fields in China and Yemen. This model is a modified ANFIS (Adaptive Neuro-Fuzzy Inference System), which is developed by applying a new optimization algorithm called the Aquila Optimizer (AO). The AO is a recently proposed optimization algorithm that was inspired by the behavior of Aquila in nature. The developed model, called AO-ANFIS, was evaluated using real-world datasets provided by local partners. In addition, extensive comparisons to the traditional ANFIS model and several modified ANFIS models using different optimization algorithms. Numeric results and statistics have confirmed the superiority of the AO-ANFIS over traditional ANFIS and several modified models. Additionally, the results reveal that AO is significantly improved ANFIS prediction accuracy. Thus, AO-ANFIS can be considered as an efficient time series tool.

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