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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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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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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Tourism Demand Modelling and Forecasting: A Review of Literature

African Journal of Hospitality, Tourism and Leisure ◽

10.46222/ajhtl.19770720.168 ◽

2021 ◽

Vol 10(4) (10(4)) ◽

pp. 1370-1393

Author(s):

Musonera Abdou ◽

Edouard Musabanganji ◽

Herman Musahara

Keyword(s):

Time Series ◽

Spatial Regression ◽

Demand Forecasting ◽

Tourism Demand ◽

New Developments ◽

Forecasting Accuracy ◽

Forecasting Models ◽

Forecasting Performance ◽

Modelling And Forecasting ◽

Forecasting Techniques

This research examines 145 key papers from 1979 to 2020 in order to gain a better sense of how tourism demand forecasting techniques have changed over time. The three types of forecasting models are econometric, time series, and artificial intelligence (AI) models. Econometric and time series models that were already popular in 2005 maintained their popularity, and were increasingly used as benchmark models for forecasting performance assessment and comparison with new models. In the last decade, AI models have advanced at an incredible rate, with hybrid AI models emerging as a new trend. In addition, some new developments in the three categories of models, such as mixed frequency, spatial regression, and combination and hybrid models have been introduced. The main conclusions drawn from historical comparisons forecasting methods are that forecasting models have become more diverse, that these models have been merged, and that forecasting accuracy has improved. Given the complexities of predicting tourism demand, there is no single approach that works well in all circumstances, and forecasting techniques are still evolving.

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PEMODELAN PRODUKSI BAWANG MERAH DI JAWA TENGAH DENGAN MENGGUNAKAN HYBRID AUTOREGRESSIVE INTEGRATED MOVING AVERAGE – ADAPTIVE NEURO FUZZY INFERENCE SYSTEM

Jurnal Gaussian ◽

10.14710/j.gauss.v7i3.26661 ◽

2018 ◽

Vol 7 (3) ◽

pp. 281-292

Author(s):

Inas Husna Diarsih ◽

Tarno Tarno ◽

Agus Rusgiyono

Keyword(s):

Fuzzy Inference ◽

Moving Average ◽

Arima Model ◽

Anfis Model ◽

Inference System ◽

Central Java ◽

Neuro Fuzzy ◽

National Production ◽

Anfis Method ◽

Method Model

Red onion is one of the strategic horticulture commodities in Indonesia considering its function as the main ingredients of the basic ingredients of Indonesian cuisine. In an effort to increase production to supply national necessary, Central Java as the main center of red onion production should be able to predict the production of several periods ahead to maintain the balance of national production. The purpose of this research is to get the best model to forecast the production of red onion in Central Java by ARIMA, ANFIS, and hybrid ARIMA-ANFIS method. Model accuracy is measured by the smallest RMSE and AIC values. The results show that the best model to modeling red onion production in Central Java is obtained by hybrid ARIMA-ANFIS model which is a combination between SARIMA ([2], 1, [12]) and residual ARIMA using ANFIS model with input et,1, et,2 on the grid partition technique, gbell membership function, and membership number of 2 that produce RMSE 12033 and AIC 21.6634. While ARIMA model yield RMSE 13301,24 and AIC 21,89807 with violation of assumption. And the ANFIS model produces RMSE 14832 and AIC 22,0777. This shows that ARIMA-ANFIS hybrid method is better than ARIMA and ANFIS.Keywords: production of red onion, ARIMA, ANFIS, hybrid ARIMA-ANFIS

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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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IMPROVISASI MODEL ARIMAX-ANFIS DENGAN VARIASI KALENDER UNTUK PREDIKSI TOTAL TRANSAKSI NON-TUNAI

Indonesian Journal of Statistics and Its Applications ◽

10.29244/ijsa.v4i2.603 ◽

2020 ◽

Vol 4 (2) ◽

pp. 296-310

Author(s):

Muhammad Luthfi Setiarno Putera

Keyword(s):

Time Series ◽

Fuzzy Inference ◽

Time Series Models ◽

Fuzzy Inference Systems ◽

Forecasting Accuracy ◽

Neuro Fuzzy ◽

Cash Payment ◽

Cash Transaction ◽

Inference Systems ◽

Accuracy Performance

Developed information technology boosts interest to use non-cash payment media in many areas. Following the high usage of a non-cash scheme in many payment transactions recently, the objective of this work is two-fold that is to predict the total of a non-cash transaction by using various time-series models and to compare the forecasting accuracy of those models. As a country with a mostly dense Moslem population, plenty of economical activities are arguably influenced by the Islamic calendar effect. Therefore the models being compared are ARIMA, ARIMA with Exogenous (ARIMAX), and a hybrid between ARIMAX and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). By taking such calendar variation into account, the result shows that ARIMAX-ANFIS is the best method in predicting non-cash transactions since it produces lower MAPE. It is indicated that non-cash transaction increases significantly ahead of Ied Fitr occurrence and hits the peak in December. It demonstrates that the hybrid model can improve the accuracy performance of prediction.

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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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