• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    A New Hybrid SARFIMA-ANN Model for Tourism Forecasting

    2022-08-23 02:17:16TanzilaSabaMirzaNaveedShahzadSoniaIqbal3AmjadRehmanandIbrahimAbunadi
    Computers Materials&Continua 2022年6期

    Tanzila Saba,Mirza Naveed Shahzad,Sonia Iqbal3,Amjad Rehman and Ibrahim Abunadi

    1Artificial Intelligence&Data Analytics Research Lab(AIDA),CCIS Prince Sultan University,Riyadh,11586,Saudi Arabia

    2Department of Statistics,University of Gujrat,Gujrat,50700,Pakistan

    3Department of Statistics,University of Sialkot,Sialkot,51310,Pakistan

    Abstract: Many countries developed and increased greenery in their country sights to attract international tourists.This planning is now significantly contributing to their economy.The next task is to facilitate the tourists by sufficient arrangements and providing a green and clean environment; it is only possible if an upcoming number of tourists’arrivals are accurately predicted.But accurate prediction is not easy as empirical evidence shows that the tourists’arrival data often contains linear, nonlinear, and seasonal patterns.The traditional model, like the seasonal autoregressive fractional integrated moving average(SARFIMA),handles seasonal trends with seasonality.In contrast,the artificial neural network(ANN)model deals better with nonlinear time series.To get a better forecasting result,this study combines the merits of the SARFIMA and the ANN models and the purpose of the hybrid SARFIMA-ANN model.Then,we have used the proposed model to predict the tourists’arrival in New Zealand,Australia,and London.Empirical results showed that the proposed hybrid model outperforms in predicting tourists’arrival compared to the traditional SARFIMA and ANN models.Moreover,these results can be generalized to predict tourists’arrival in any country or region with a complicated data pattern.

    Keywords:SARFIMA;hybrid model;tourists’arrival forecasting;ANN

    1 Introduction

    The rapid development in the tourism industry in the last 30 years has contributed to many countries’economies.According to the World Travel&Tourism Council(WTTC),2019,tremendous evolution was observed in the tourism industry.It has created 313 million jobs and created prosperity in industries related to tourism and has increased taxations.As a result, the tourism industry had generated 10.3%of the global GDP.Consequently,every country is paying much attention to growing tourists’arrival in their territory.Therefore, tourism policy makers and business practitioners are interested in knowing an accurate forecast of tourist volume for properly distributing resources and formulating pricing strategies[1].

    One well-known approach to model tourists’arrival data is to use time series models.For this purpose,models based on the Box-Jenkins methodology have been extensively used in the past several decades.For example, the autoregressive integrated moving average (ARIMA) and its sub-models are well-recognized to predict future observations based on linearly correlated past observation and a white noise error term.However, these models work efficiently only for short memory time series processes[2].On the other hand,most time-series data,including financial data,stock-exchange data,and numbers of tourists’arrivals,have long memory characteristics.For such types of time series data,Granger [3] introduced the autoregressive fractional integrated moving average (ARFIMA) model that speculates the ARIMA by allowing non-integer values of the differencing parameter(d).Recall,Robinson [4], that the long memory in time series is termed autocorrelation at long lags, and the strength of this long memory is measured bydof the ARFIMA model.

    The ARFIMA model has been applied in several studies for accurate forecasting.For example,Doornik et al.[5] forecasted US and UK inflation rates using the ARFIMA model and showed its superiority over the ARIMA model.Chu[6]has found better forecasting about tourists’demand in Hong Kong, Japan, and Korea by ARFIMA model than ARIMA.Moreover, Cheung [7] used the ARFIMA model for predicting the foreign exchange rates.Along with the ARFIMA model, Prass et al.[8] also applied the seasonal autoregressive fractional integrated moving average (SARFIMA)model to predict the mean monthly water level Paraguay River in Brazil.The SARFIMA model has also been used by Mostafaei et al.[9] to predict Iran’s oil supply.In recent years, Peng et al.[10]developed the new hybrid random forest-LSTM model to forecast tourist arrival data and justified by the Beijing city and Jiuzhaigou valley data that this hybrid approach outperforms.Waciko et al.[11] used the Thief-MLP hybrid approach to forecast short-term tourists’arrival to Bali-Indonesia.Wu et al.[12]forecasted daily tourist arrival to Macau SAR,China with a hybrid SARIMS–LSTM approach and obtained the good fitted results.

    It is important to note that all Box-Jenkins models require the linearity of the process under study(see, e.g., [13]).This means that future observations should have a linear relationship with present and past observations.Therefore,ARFIMA and SARFIMA models become inappropriate when time series are generated from nonlinear processes.Nonlinear models such as the Artificial Neural network(ANN) have been used in several studies to overcome this problem.For instance, Sarvareddy et al.[14]studied the characteristics of ANN and showed its forecasting power over traditional time series models.Zhang et al.[15]studied ANN forecasting for seasonal and trend time series and showed that the ANN has nonlinear architecture and performs well for linear time series.In several studies,ANN has been constantly compared with the traditional Box-Jenkins model and long memory models,and its better performance is justified.Prybutok et al.[16]observed that the ANN analysis performs better for time series datasets,specifically in the presence of nonlinearity.

    Note that there is no definitive proper measure to check whether the process generated from time series is linear or nonlinear.Therefore,it would be difficult to decide in advance which model should be used.Furthermore,there is no standard model that is appropriate for both linear and nonlinear time series data.Therefore,by combining the characteristics of two or more models,the accuracy can be increased.Different hybrid models have been developed following this idea,particularly by combining linear autoregressive models and ANN models.The beauty of these types of hybrid models is that they can capture the characteristics of both linearity and non-linearity in the time series[17].This idea was first used by Zhang[18]to introduce a hybrid methodology that combines both ARIMA and ANN models.Following[18],a hybrid of ARIMA models and support vector machines(SVMs)were used by Pai et al.[19]to forecast stock prices.Chen et al.[20]examined the forecasting accuracy of the hybrid of seasonal ARIMA (SARIMA) and SVM models for Taiwan’s machinery industry production.Chaabane[21]found a hybrid of ARFIMA and ANN model more efficient over individual ARFIMA and ANN to predict electricity price.

    Moreover, Chaabane [22] found a better performance of hybrid ARFIMA–least-squares SVM than the competing models while predicting electricity spot prices.In general,practitioners rely on two methods to forecast tourist volume.One is time series analysis methods[23]and the other is artificial intelligence methods[24–26].However,many recent studies have proved that the combination of the aforementioned methods leads to better forecasts[27–29].

    In the present study,our main interest is to introduce a new hybrid model for better and accurate prediction using the complicated time series data, the new hybrid model is proposed by hybridizing the SARFIMA and ANN model using the Zhang [18] approach.The second goal is to evaluate the predictability of the developed model on the different natured tourists’arrival datasets, for this,the datasets of the three countries were retrieved and better results have been obtained.Finally, the performance of the suggested model is verified using out-of-sample data as real-time analysis,and the results predicted by the model matched with the real tourists’arrival values.

    The rest of the paper is organized as follows.An overview of ARFIMA,SARFIMA,and ANN models is given in Sections 2,3,and 4,respectively.The proposed hybrid SARFIMA-ANN model is presented in Section 5.Section 6 reports on the proposed hybrid model’s empirical results using three real datasets on tourists’arrival and discussion.The concluding remarks are given in Section 7.

    2 ARFIMA(p,d,q)Model

    When there is a long memory presence in time series data, the frequently used model is the ARFIMA model, introduced by Granger et al.[30] and its properties were further investigated by Baillie[31].Let us assume that{yt}t∈Wis a stationary process with the white noise error termεtwith zero mean and varianceσ2>0.LetBbe the backshift operator defined byBk(yt):=yt-k.Further,assume that{yt}t∈Wis a linear process satisfying the following relationship

    whered∈(-0.5,0.5), ψ(·)and?(·)are the following polynomials of degreepandqrespectively,that is

    where ψk,1 ≤k≤pand?l, 1 ≤l≤qare real constant terms.Then the series {yt}t∈Wfollows a fractional differencing model ARFIMA(p,d,q).Note that the ARFIMA(p,d,q)model is an improved form of the traditional ARIMA(p,d,q)model due to considering fractional differencing parameterd.Ifd∈(-0.5,0.5),the series{yt}t∈Wsatisfies the property of stationary and inevitability and indicates a long memory process.On the other hand,d= 0 indicates a short memory, whereasd∈(-0.5,0)tells that the process has an intermediate memory level.

    3 SARFIMA(p,d,q)(P,D,Q)s Model

    There are several situations in which time-series data have a long memory and exhibit periodic patterns.The appropriate model for such time-series data is the SARFIMA(p,d,q)(P,D,Q)s, an extended form of the ARFIMA process[32].The following are propositions that need to be satisfied to consider SARFIMA as an appropriate model.

    Proposition 1:Let{yt}t∈Wbe the stochastic stationary process following spectral density functionfy(.).Suppose that there is a real quantityt∈(0,1),a constantCfandK∈ [0,π](with one or a finite number of occurrences)such thatfy(ω)~Cf|ω-K|-tWhenω→G.Then {yt}t∈Wfollow a long memory process.

    When t ∈(-1,0),then the processytis said to be an intermediate memory process[33].

    Proposition 2:Let{yt}t∈Wbe a seasonal stationary process with

    where ψk,1 ≤k≤p,?h,1 ≤l≤Q,ζl,1 ≤l≤P,and?g,1 ≤g≤Qare constant integers with ψ0=ζ0=?0=?0=-1.

    Proposition 3:Assume that{yt}t∈Wis the SARFIMA(p,d,q)(P,D,Q)sprocess,with mean zero and seasonal period s ∈N.Suppose ψ(W)ζ(Ws)= 0 and?(W)?(Ws)= 0,have no zero in common.Then,the following axioms are factual:

    i.The process {yt}t∈Wfollows the stationary process whenD <0.5,(d+D) <0.5, and ψ(W)ζ(Ws)0 for|W|≤1.

    ii.The stationary process {yt}t∈Wfollows long memory process when(d+D)∈(0,0.5),D∈(0,0.5)and?(W)?(Ws)0 for|W|≤1.

    iii.The stationary process{yt}t∈Wfollows long memory process when(d+D)∈(-0.5,0.5),D∈(-0.5,0)and ψ(W)ζ(Ws)0 for|W|≤1.

    Based on Katayama’s previous work[34],model estimation using SARFIMA requires a few steps.Firstly,identifying the long memory process and finding fractional difference parameterd.Secondly,identifying the model and estimating parameters,and finally,applying some diagnostic checks.After obtaining a suitable model,it can be used for predictive purposes.

    4 Artificial Neural Network Based Forecasting

    When the restriction of linearity on time series data is relaxed,enormous nonlinear models have been developed for obtaining better forecasts.An artificial neural network(ANN)is one of them.The critical characteristic of ANN over other nonlinear models is its ability to deal with a large class of functions.Moreover,ANN does not require any prior assumption for the estimation process.Instead,its architecture is entirely determined from the characteristic of data.For more details on ANN, we refer the interested readers to[35].

    The ANN architecture consists of an input layer, an output layer, and multiple hidden layers depending upon the complexity of data.Information passes through each layer in terms of neurons.For forecasting time series data (yt), the simplest neural network architecture takes lagged values(yt-1,yt-2,yt-3,....,yt-p)as input.We used the Rabie et al.[36]heuristic/Ad-hoc input selection criteria to select the input nodes for ANN.Then the nonlinear relationship between outputytand input values(yt-1,yt-2,yt-3,....,yt-p)is defined by

    whereεtis an error term with zero mean and standard deviationσt,αjandβijfori=0, 1, 2,...,vandj=0, 1, 2,...,uare the model parameters to be estimated.They are also known as connection weights.The functiongis known as the activation function used in hidden layers to transfer the input if a certain threshold is met.One can use different activation functions like hyperbolic,sigmoid,etc.However,the most popular is the sigmoid,which is defined by

    The ANN model in(3)can be termed as univariate nonlinear autoregressive(NAR)model,that is:

    Heref(.)is a function estimated by the network structure and connection weights,andvis a vector of all parameters.The ANN model(3)is quite powerful in terms of its architecture as it can estimate the arbitrary function by increasing the number of hidden nodesv.

    Since there is no standard mechanism to determine the appropriate ANN architecture for the given data,multiple experiments can be conducted to choose suitable values forpandq.After selectinguandv,the model is ready for training and to estimate the network parameters.

    5 Hybrid Methodology and the Proposed Hybrid Method:SARFIMA-ANN

    The tourists’arrival time series data may consist of many components such as linearity,nonlinearity,seasonality,heteroscedasticity,or a non-normal error.One standard approach for forecasting such time-series data dealing with all components does not exist.One may think SARFIMA is a better option for this case.However,SARFIMA cannot deal with complex nonlinear structures.The second choice might be the ANN, which deals well with nonlinear structure but may also provide unsatisfactory results when modeling the linear data[37].In other words,both SARFIMA and ANN models are successful only in their domains.Zhang[18]introduced hybrid models that can model both linear and nonlinear structures of time series data to overcome this problem.

    Following Zhang[18],we propose a hybrid of the SARFIMA and ANN model in this study.In the proposed model,time-series data is composed as a function of linear and nonlinear components.To be more precise,yt:=f(Nt,Lt), whereNtis termed as nonlinear part andLtis termed as linear part.It is important to note that the hybrid approach is one of the efficient approaches that provide high accuracy rate in forecasts by establishing an additive relationship between a linear and nonlinear component of data,that is,

    The different methods can estimate the linear and nonlinear parts of(5)to develop the model.The defined methodology used in this work has three steps.In the first step, the linear portion of the time series data is modeled by SARFIMA, considering it follows a long memory process.From the fitted SARFIMA model,the forecasted valuesare obtained.In the second step,residuals from the SARFIMA model are generated and, under the assumption that these residuals exhibit nonlinear patterns, an ANN model is trained.To be more precise, the input-output relationship is ANN is estimated by the following relationship,

    whereg(.) is a nonlinear regression function determined by the ANN model.This provides us the prediction of the nonlinear partIn the last step,predictions of the linear and nonlinear componentLtandNt,are combined to generate the cumulative prediction,that is,

    The pictorial representation of our proposed hybrid methodology is given in Fig.1.In addition,the algorithm of hybrid SARFIMA-ANN is presented in Fig.1.

    Figure 1:Hybrid SARFIMA-ANN model

    6 Application and Empirical Results

    To check the performance of the proposed hybrid SARFIMA-ANN model on forecasting tourists’arrival,we consider the three real data sets.As,over the past three decades,tourism has become one of the world’s most flourishing industries.International tourists’arrival has conventionally been used as a benchmark to assess any country’s security condition and economic development.It significantly impacts GDP, employment rate, import and export, and many public and private sectors.This significant impact attracts the researcher to study the flow of tourists’arrival in a particular country.The number of tourists’arrivals can be considered a time series process due to the consistent change over time and therefore,the prediction model may be applied.Tourists’arrival data get more attention in several studies(see,[38–40]).In this study,the following three tourists’arrival datasets are considered to implement and justify the proposed hybrid model’s competency over the other models.

    Dataset 1.This dataset is related to the tourism industry,growing gradually in New Zealand due to its amazing natural attraction sites.To forecast tourists’arrival in this country,monthly data from January 2000 to September 2018 is retrieved from www.stats.govt.nz,a sample of 225 observations.The plot of this dataset in Fig.2 depicts that the considered data is stationary in the mean but has seasonal variation.The autocorrelation function and partial autocorrelation function showed the presence of seasonality ats=6.

    Figure 2:Monthly data of tourists’arrivals in New Zealand from January 2000 to September 2018

    Dataset 2.The second dataset contains the monthly number of tourists’arrival in Australia from January 2000 to August 2018,giving 224 observations,which are retrieved from www.abs.gov.au.The data series is regarded as nonlinear and non-Gaussian and suitable to evaluate for analysis.This timeseries data has been plotted in Fig.3,showing seasonality ats=6 with the observed trend.The data is non-stationary and the first difference is taken of the data for further analysis.

    Figure 3:Monthly data of tourists’arrivals in Australia from January 1976 to August 2018

    Dataset 3.The proposed model is also applied to the number of tourists’ arrival in London, UK.The quarterly data set has 66 observations, corresponding to 2002Q1–2018Q2, taken from www.data.london.gov.uk and plotted in Fig.4.There exist seasonal fluctuations ats=64 in the series.For modeling and forecasting from this series,the first difference of the data is taken.

    Figure 4:Quarterly data of tourists’arrivals(000s)in London from 2002Q1 to 2018Q2

    These three datasets are used in the present study to demonstrate the effectiveness of the proposed hybrid method.Note that these tourists’arrival datasets have seasonal fluctuations due to seasonal changes and this situation requires explaining such fluctuations by some suitable seasonal models.The summary of the datasets is given in Tab.1.The considered datasets are far from normality as indicated by skewness and kurtosis values and further confirmed by the Jarque-Bera test.Furthermore,Augmented Dickey-Fuller,Philips–Perron,and Kwiatkowski,Phillips,Schmidt,and Shin tests indicate that dataset 1 is stationary whereas the other two datasets are non-stationary.To make the datasets suitable for analysis, datasets are made stationary by taking first differences.Then, long memory parameterdand Hurst parameterHare estimated to ensure that the considered data sets follow long memory processes.For tourists’arrival datasets of New Zealand,Australia,and London,the estimated values fordare 0.4812, 0.3704, and 0.2351, and forHare 0.9812, 0.8715, and 0.7338, respectively.Since 0<d <0.5 andH >0.5,the criteria explained in Proposition 1 is satisfied.This ensures that the considered datasets came from long memory processes.

    Table 1: Descriptive statistics for the tourists’arrival datasets in three selected stations

    Table 1:Continued

    In order to apply and explain the performance of SARFIMA, ANN, and hybrid SARFIMAANN models, the datasets are partitioned into the training and testing part.To be more precise,New Zealand tourists’arrival data from January 2000 to December 2015 (85.71%) is considered to train models and the set from January 2016 to August 2018(14.29%)is considered for model testing.Similarly,Australian tourists’arrival data from January 2000 to December 2015(87.71%)is used for model training, and the rest from January 2016 to August 2018 (14.29%) is considered for model testing.Similarly, in London tourists’arrival data, set from first quarter 2002 to fourth quarter of 2013 (72.73%) is considered a training set and from first quarter 2014 to the second quarter, 2018(27.27%)is considered a testing set.This partition of datasets is also sketched in Fig.5.

    Figure 5: Continued

    Figure 5:Partition of tourists’arrival into training and testing part

    6.1 SARFIMA Model for Tourists’Arrival Datasets

    The fitting of SARFIMA(p,d,q)(P,D,Q)smodel on training part of tourists’arrival datasets requires suitable values forp,q,PandQ.In the case of New Zealand and Australian tourists’arrival datasets, we takep= 1,2, 3, 4;P= 10, 11, 13;q= 1, 2 andQ= 7, 8, 9, 13, 14 on the basis of autocorrelation functions(ACF)and partial autocorrelation function(PACF)values and pattern.Then we take all possible combinations ofp,q,PandQ,and fitted 120 SARFIMA models.Analogously, based on ACF and PAF, the suitable values for the London tourist arrival dataset arep= 1,q= 1,2,P= 4,8,Q= 4,ands= 4.For this dataset,we further estimate SARFIMA models with all possible combinations ofp,q,PandQ.After fitting all possible models on each dataset,we have selected models for each dataset based on the minimum values of Akaike Information criteria(AIC) and Bayesian Information Criteria (BIC), keeping in view the values ofdandDsatisfy the stationary and long memory process condition.Tab.2 presents these four best-fitted models along with their ranks.The first ranked models have been observed as the most parsimonious models among all SARFIMA models for each dataset.

    Table 2: SARFIMA model selection for tourists’arrival datasets

    Table 2:Continued

    6.2 Artificial Neural Network Model

    To obtain the most accurate ANN model, numerous ANN models were established for the considered datasets using two hidden layers with varying 2 to 30 nodes in the first hidden layer,and 2 to 7 nodes in the second hidden layer.By varying the number of nodes in the first and second hidden layers,145 models are developed,whereas each model is trained 50 times.Due to the space limitation,the detailed results are not presented here.However,from these models,the best models are selected based on minimum mean squared error(MSE)and root mean squared error(RMSE).Consequently,we obtain ANN(6×2×1),ANN(4×2×1)and ANN(10×2×1)for New Zealand,Australia,and London tourists’arrival datasets, respectively.The prediction from the best ANN model for each dataset is presented in Fig.6 for comparison.

    Figure 6:Prediction comparsion among SARFIMA,ANN and hybrid SARFIMA-ANN models(----predicted and––actual values)

    6.3 Hybrid SARFIMA-ANN Model

    The hybrid algorithm mainly consisted of two steps as discussed earlier.In the first step, a SARFIMA model is fitted to analyze the linear part of the data and in the second step,the residuals from the SARFIMA model are analyzed.Linearity in the residuals is checked using the BDS test as suggested by Broock et al.[41].In order to perform the BDS test, the following steps are being followed.

    i.Select embedded dimensions(m)value so that the embed time series transforms intomdimensional vectors by considering eachmsucceeding point in the series.

    ii.Calculate the correlation coefficient thatm-dimensional hyperspace for the proportion of points within a distance ∈of each other.

    HereI∈=where ||·||used for supremum norm.BDS test illustrates that if the null hypothesis demonstratesxtseries is i.i.d.,thenUm,I(∈)-Um,L(∈)mwith probability one as the sample size tends to infinity and ∈tends to zero.

    iii.Compute BDS test statistic that is defined as

    iv.It is a two-tail test.The statement under the null hypothesis will reject when the BDS test statistic is greater than the critical value.

    In Tab.3,the results of the BDS test on residuals from selected models SARFIMA models such as SARFIMA(3,0,1)(10,0,7)6,SARFIMA(1,0,3)(5,0,6)6and SARFIMA(2,1,1)(5,0,6)4are presented for tourists’arrival datasets of New Zealand,Australia,and London.

    Table 3: BDS test results on residuals from the SARFIMA model

    The numerical results of the BDS test,as in Tab.3,favour rejecting the null hypothesis about the time series linearity at a 5%level of significance.It demonstrates that the errors(residuals)from the best selected SARFIMA models have nonlinear patterns.This indicates that only the linear model(SARFIMA model)is not adequate to model the data well.Therefore,implementation of nonlinear models also requires,such as ANN,to capture the nonlinearity pattern.

    We consider the best selected SARFIMA and ANN models,and build the hybrid SARFIMA and ANN models for New Zealand,Australia,and London tourists’arrival data.Recall Zhang[18]that one can use separate suboptimal models to develop the hybrid method.Following their suggestion,the optimal SARFIMA model is used to model the linear part of the data and the nonlinear patterns fitted by the finalized ANN model.Then the improved prediction is obtained by combining the output of the best fitted SARFIMA and ANN model in the hybrid SARFIMA-ANN model.The performance indicators, that is, MSE and RMSE of the proposed hybrid SARFIMA-ANN and the individuals SARFIMA and ANN models, are presented in Tab.4.The comparison of the results clearly shows that the proposed hybrid SARFIMA-ANN outperforms than the competing models.Furthermore,the out-of-sample performance of the hybrid and individual models is shown in Fig.6.We see that the forecasting obtained by the hybrid SARFIMA-ANN model in each dataset is closer to actual than the competing models.

    Table 4: The best-fitted models on the training part for all three tourists’arrival datasets

    7 Conclusion

    Tourism is a rapidly growing industry in most countries and it is demanding more accurate modeling and forecasting of tourists’arrival data for many purposeful decisions.This, in turn, has grabbed increasing attention to more accurate and advanced forecasting methods.Therefore,the main interest of the study was to establish a possibility for the improvement in the forecast accuracy of tourist arrival using a hybrid modeling approach.Recently,the extension of the SARIMA,which is called the SARFIMA model, has become popular for the linear time series data with long memory processes and periodic patterns.More recently,the ANNs have shown much flexibility in modeling the nonlinear data.Therefore,ANN and SARFIMA can only achieve accurate results in their premises,and generally, none of them is the best model for every forecasting situation.Thus, in this study, a hybrid SARFIMA-ANN approach is established and applied to forecast tourists’arrival in Australia,New Zealand, and London.This approach is outperformed and produced promising results in the actual situation and produces positive results compared to the two competitors,SARFIMA and ANN.Our results have implications both for theory and application.Theoretically, we developed a hybrid SARFIMA-ANN model.In terms of application, the results of hybrid SARFIMA-ANN provide confidence for policymakers in the search volumes of tourists’arrival.Consequently, the proposed hybrid SARFIMA-ANN model and the investigation of this study make a good step in improving the forecast accuracy in tourists’arrival.

    Acknowledgement:This research is supported by Artificial Intelligence & Data Analytics Lab(AIDA) CCIS Prince Sultan University, Riyadh 11586 Saudi Arabia.The authors also would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges(APC)of this publication.

    Funding Statement:The authors received no specific funding for this study.

    Conflicts of Interest:The authors declare that they have no interest in reporting regarding the present study.

    Appendix

    A hybrid algorithm based on SARFIMA and Neural network

    中文字幕高清在线视频| 18美女黄网站色大片免费观看| 亚洲国产毛片av蜜桃av| 12—13女人毛片做爰片一| 久久久久国产精品人妻aⅴ院| 精品国产国语对白av| 黄色怎么调成土黄色| 国产精品一区二区在线不卡| 亚洲avbb在线观看| e午夜精品久久久久久久| 国产一区二区激情短视频| 美女高潮喷水抽搐中文字幕| 久久久久久人人人人人| 女同久久另类99精品国产91| 一本综合久久免费| 国产片内射在线| 久久草成人影院| 中文字幕另类日韩欧美亚洲嫩草| 国产欧美日韩一区二区三| 国产不卡一卡二| 亚洲国产欧美日韩在线播放| 亚洲一区二区三区不卡视频| 国产片内射在线| 久久香蕉激情| а√天堂www在线а√下载| 悠悠久久av| 色播在线永久视频| 欧美黑人欧美精品刺激| 国产高清视频在线播放一区| 在线观看免费视频日本深夜| 国产激情欧美一区二区| 国产亚洲欧美在线一区二区| 18禁国产床啪视频网站| 美女午夜性视频免费| 午夜激情av网站| 亚洲一区二区三区不卡视频| 午夜91福利影院| 亚洲全国av大片| 又大又爽又粗| 亚洲成人免费av在线播放| www.精华液| 村上凉子中文字幕在线| 欧美国产精品va在线观看不卡| 国产精品秋霞免费鲁丝片| 久久精品人人爽人人爽视色| 天天影视国产精品| 国产高清视频在线播放一区| 黄片播放在线免费| 99riav亚洲国产免费| 国产精品爽爽va在线观看网站 | 日本三级黄在线观看| 91在线观看av| 亚洲一区二区三区不卡视频| 国产免费男女视频| 乱人伦中国视频| 免费av毛片视频| 19禁男女啪啪无遮挡网站| 99在线人妻在线中文字幕| 日韩精品免费视频一区二区三区| 日韩欧美一区二区三区在线观看| 国产精品日韩av在线免费观看 | 亚洲精品美女久久久久99蜜臀| 久久久精品欧美日韩精品| 亚洲一码二码三码区别大吗| 香蕉国产在线看| 桃色一区二区三区在线观看| 美女扒开内裤让男人捅视频| 午夜日韩欧美国产| 悠悠久久av| 男人舔女人下体高潮全视频| 午夜福利免费观看在线| 啪啪无遮挡十八禁网站| 桃色一区二区三区在线观看| 多毛熟女@视频| 国产1区2区3区精品| 又黄又爽又免费观看的视频| 午夜视频精品福利| 中文字幕人妻丝袜一区二区| 中亚洲国语对白在线视频| 免费人成视频x8x8入口观看| 亚洲精品一二三| 极品教师在线免费播放| 操美女的视频在线观看| 日日干狠狠操夜夜爽| videosex国产| 夫妻午夜视频| 午夜福利欧美成人| 亚洲国产看品久久| 欧美亚洲日本最大视频资源| 老熟妇仑乱视频hdxx| 三上悠亚av全集在线观看| 妹子高潮喷水视频| 亚洲人成网站在线播放欧美日韩| 美女福利国产在线| 午夜福利影视在线免费观看| 欧美日韩瑟瑟在线播放| 国产视频一区二区在线看| 久热爱精品视频在线9| 亚洲精品久久午夜乱码| 亚洲精品久久午夜乱码| 看片在线看免费视频| 久久香蕉激情| 免费女性裸体啪啪无遮挡网站| 十八禁网站免费在线| 久久人人精品亚洲av| 自线自在国产av| 嫩草影视91久久| 日韩精品青青久久久久久| 在线观看www视频免费| 天堂俺去俺来也www色官网| 久久精品亚洲熟妇少妇任你| 免费观看精品视频网站| 国产片内射在线| 国产精品爽爽va在线观看网站 | 婷婷精品国产亚洲av在线| 美女高潮喷水抽搐中文字幕| 欧美成人性av电影在线观看| 色综合欧美亚洲国产小说| 亚洲av成人一区二区三| 无限看片的www在线观看| 亚洲av五月六月丁香网| 久久久国产欧美日韩av| 亚洲av电影在线进入| 黄片大片在线免费观看| 如日韩欧美国产精品一区二区三区| 国产亚洲欧美精品永久| 国产亚洲精品一区二区www| 免费在线观看亚洲国产| 国产高清videossex| 欧美日韩国产mv在线观看视频| 又紧又爽又黄一区二区| 欧美日韩黄片免| 色在线成人网| 成人三级黄色视频| 最近最新中文字幕大全免费视频| 色综合欧美亚洲国产小说| 午夜日韩欧美国产| 亚洲专区中文字幕在线| 男女床上黄色一级片免费看| 麻豆国产av国片精品| 欧美日韩福利视频一区二区| 中文字幕av电影在线播放| 一级黄色大片毛片| 一a级毛片在线观看| 他把我摸到了高潮在线观看| 亚洲五月天丁香| 99精品久久久久人妻精品| 成年人黄色毛片网站| 男女高潮啪啪啪动态图| 欧美日韩瑟瑟在线播放| 国产精品香港三级国产av潘金莲| 久久天躁狠狠躁夜夜2o2o| www.精华液| 精品无人区乱码1区二区| av国产精品久久久久影院| 精品一区二区三区视频在线观看免费 | 国产亚洲av高清不卡| 亚洲精品av麻豆狂野| 在线国产一区二区在线| 国产人伦9x9x在线观看| 天堂影院成人在线观看| 欧洲精品卡2卡3卡4卡5卡区| 十八禁人妻一区二区| 国产一区二区激情短视频| 999久久久国产精品视频| 亚洲中文日韩欧美视频| 国产高清videossex| 国产激情久久老熟女| 中文字幕最新亚洲高清| 真人一进一出gif抽搐免费| 99久久精品国产亚洲精品| 91大片在线观看| 亚洲av成人一区二区三| 国产成人免费无遮挡视频| 国产激情欧美一区二区| 在线十欧美十亚洲十日本专区| 久久人人97超碰香蕉20202| 久久人人97超碰香蕉20202| 欧美在线黄色| 午夜福利在线免费观看网站| 日韩大码丰满熟妇| 桃色一区二区三区在线观看| 国产免费现黄频在线看| 色在线成人网| 美女大奶头视频| a级片在线免费高清观看视频| 在线免费观看的www视频| 天堂动漫精品| 19禁男女啪啪无遮挡网站| 亚洲九九香蕉| 90打野战视频偷拍视频| 黑丝袜美女国产一区| 18禁美女被吸乳视频| 在线看a的网站| 啦啦啦 在线观看视频| 日本a在线网址| 一级,二级,三级黄色视频| 久久精品亚洲av国产电影网| 老司机午夜十八禁免费视频| 两性夫妻黄色片| 国产精品美女特级片免费视频播放器 | xxxhd国产人妻xxx| 97碰自拍视频| 51午夜福利影视在线观看| 欧美乱妇无乱码| 精品一区二区三区av网在线观看| 亚洲精品一卡2卡三卡4卡5卡| 国产精品国产高清国产av| 欧美日韩亚洲高清精品| 国产精品九九99| 国产中年淑女户外野战色| 日本黄色片子视频| 国产精品一区二区三区四区免费观看 | 男女那种视频在线观看| 国产亚洲精品综合一区在线观看| 国产激情偷乱视频一区二区| 国产精品伦人一区二区| 精品人妻视频免费看| 丰满乱子伦码专区| 少妇人妻精品综合一区二区 | 亚洲不卡免费看| 婷婷亚洲欧美| 少妇丰满av| 国产高清有码在线观看视频| 欧美成人免费av一区二区三区| 亚洲成a人片在线一区二区| 欧美日韩国产亚洲二区| 婷婷色综合大香蕉| 国产伦人伦偷精品视频| 内地一区二区视频在线| 小说图片视频综合网站| 麻豆国产97在线/欧美| 久久午夜亚洲精品久久| 欧美潮喷喷水| 亚洲欧美日韩东京热| 午夜福利欧美成人| 亚洲无线在线观看| 亚洲不卡免费看| 欧美最新免费一区二区三区 | 成人毛片a级毛片在线播放| www日本黄色视频网| 欧美性猛交╳xxx乱大交人| 嫩草影院入口| 精品人妻一区二区三区麻豆 | 亚洲午夜理论影院| 午夜两性在线视频| 国产精品一及| 男人的好看免费观看在线视频| 亚洲欧美清纯卡通| 欧美潮喷喷水| 日本黄色视频三级网站网址| 国产一区二区激情短视频| 亚洲av免费高清在线观看| 精品久久久久久久末码| 国产精品电影一区二区三区| 亚洲成人精品中文字幕电影| 性色av乱码一区二区三区2| 亚洲国产色片| 成人欧美大片| 亚洲乱码一区二区免费版| 美女高潮喷水抽搐中文字幕| 桃红色精品国产亚洲av| 成人一区二区视频在线观看| 美女大奶头视频| 夜夜爽天天搞| 夜夜夜夜夜久久久久| 国产伦一二天堂av在线观看| 国产一级毛片七仙女欲春2| 久久精品夜夜夜夜夜久久蜜豆| 我的女老师完整版在线观看| 51国产日韩欧美| 少妇的逼水好多| 日日摸夜夜添夜夜添av毛片 | 亚洲不卡免费看| 免费av不卡在线播放| 免费高清视频大片| 性欧美人与动物交配| 三级男女做爰猛烈吃奶摸视频| 香蕉av资源在线| 色吧在线观看| 欧美乱妇无乱码| 波多野结衣巨乳人妻| 久久人人精品亚洲av| 最近最新免费中文字幕在线| 观看免费一级毛片| 欧美区成人在线视频| 免费高清视频大片| 久久精品国产99精品国产亚洲性色| 老司机深夜福利视频在线观看| 国产不卡一卡二| 俺也久久电影网| 国产av麻豆久久久久久久| 人人妻人人澡欧美一区二区| 欧美日韩乱码在线| 免费观看精品视频网站| 国产精品久久久久久亚洲av鲁大| 欧美zozozo另类| 国产久久久一区二区三区| 12—13女人毛片做爰片一| 亚洲狠狠婷婷综合久久图片| 天堂动漫精品| 中出人妻视频一区二区| 日日摸夜夜添夜夜添av毛片 | 九九在线视频观看精品| 最近视频中文字幕2019在线8| 亚洲不卡免费看| 最近最新中文字幕大全电影3| 日本熟妇午夜| 国产黄a三级三级三级人| 狂野欧美白嫩少妇大欣赏| 亚洲最大成人中文| 变态另类丝袜制服| 成人午夜高清在线视频| 免费观看精品视频网站| 又粗又爽又猛毛片免费看| 免费人成视频x8x8入口观看| 国产亚洲精品综合一区在线观看| 中文字幕高清在线视频| 999久久久精品免费观看国产| 亚洲美女视频黄频| 可以在线观看的亚洲视频| 久久久久久久久久成人| 最新中文字幕久久久久| 成人性生交大片免费视频hd| av福利片在线观看| 欧美一区二区精品小视频在线| 99riav亚洲国产免费| 少妇的逼水好多| 午夜福利欧美成人| 亚洲第一欧美日韩一区二区三区| 我的老师免费观看完整版| 免费大片18禁| 欧美+日韩+精品| 91麻豆精品激情在线观看国产| 别揉我奶头~嗯~啊~动态视频| 亚洲人成网站在线播放欧美日韩| 18禁在线播放成人免费| 天堂影院成人在线观看| 中文字幕人成人乱码亚洲影| 成人av在线播放网站| 91在线观看av| 国产三级黄色录像| 久久精品国产亚洲av天美| 欧美日韩黄片免| 欧美成人性av电影在线观看| 夜夜躁狠狠躁天天躁| 国产精品嫩草影院av在线观看 | 成人av一区二区三区在线看| 99热只有精品国产| 久久精品国产99精品国产亚洲性色| 日韩 亚洲 欧美在线| 99久久99久久久精品蜜桃| 少妇人妻一区二区三区视频| 国产精品,欧美在线| 精品久久久久久成人av| 老熟妇乱子伦视频在线观看| 国产蜜桃级精品一区二区三区| 亚洲五月婷婷丁香| .国产精品久久| 看十八女毛片水多多多| 久久精品影院6| 欧美丝袜亚洲另类 | 婷婷六月久久综合丁香| 亚洲aⅴ乱码一区二区在线播放| 亚洲人成伊人成综合网2020| 国产黄片美女视频| 亚洲国产欧美人成| 99久久成人亚洲精品观看| 日本熟妇午夜| 色在线成人网| 久久性视频一级片| 少妇高潮的动态图| 嫩草影院新地址| 国产综合懂色| 日韩欧美精品v在线| 在现免费观看毛片| 国产精品98久久久久久宅男小说| 丰满的人妻完整版| 成年免费大片在线观看| 久久久久久久久久成人| 搡女人真爽免费视频火全软件 | 久久精品国产清高在天天线| 最近最新免费中文字幕在线| 午夜免费男女啪啪视频观看 | 国产精品av视频在线免费观看| 成年人黄色毛片网站| 夜夜躁狠狠躁天天躁| 丁香欧美五月| 欧美乱妇无乱码| 99久久九九国产精品国产免费| 日韩精品青青久久久久久| 十八禁人妻一区二区| 国产伦在线观看视频一区| 亚洲精品成人久久久久久| 精品人妻熟女av久视频| 亚洲第一区二区三区不卡| 精品免费久久久久久久清纯| 看黄色毛片网站| 热99在线观看视频| 久久久久性生活片| 在线天堂最新版资源| 色噜噜av男人的天堂激情| 国产精品美女特级片免费视频播放器| 欧美极品一区二区三区四区| 日本免费一区二区三区高清不卡| 欧美成人a在线观看| 蜜桃久久精品国产亚洲av| 最好的美女福利视频网| 老鸭窝网址在线观看| 中文资源天堂在线| 欧美一区二区国产精品久久精品| 一本一本综合久久| 久久国产精品影院| 国产成人福利小说| 久久午夜福利片| 国产探花极品一区二区| 在线免费观看的www视频| 一卡2卡三卡四卡精品乱码亚洲| 欧美又色又爽又黄视频| 偷拍熟女少妇极品色| 国内精品久久久久精免费| 欧美三级亚洲精品| 亚洲18禁久久av| 欧美性感艳星| 国产探花在线观看一区二区| 97超视频在线观看视频| 久9热在线精品视频| 亚洲专区国产一区二区| 乱人视频在线观看| 免费人成在线观看视频色| 在线天堂最新版资源| 亚洲av一区综合| 午夜视频国产福利| 国产av在哪里看| 欧美日韩乱码在线| 在线国产一区二区在线| 国产三级黄色录像| 国产视频内射| 又黄又爽又免费观看的视频| 超碰av人人做人人爽久久| 丁香六月欧美| 男人舔女人下体高潮全视频| 国产v大片淫在线免费观看| 午夜福利高清视频| 国产高清有码在线观看视频| 国内精品久久久久精免费| www日本黄色视频网| 别揉我奶头~嗯~啊~动态视频| 亚洲人成网站在线播放欧美日韩| 免费av毛片视频| 免费在线观看亚洲国产| 韩国av一区二区三区四区| 男女床上黄色一级片免费看| 内地一区二区视频在线| 亚洲成人中文字幕在线播放| 午夜激情欧美在线| 美女高潮喷水抽搐中文字幕| 免费搜索国产男女视频| 欧美成人一区二区免费高清观看| 欧美乱妇无乱码| 国产伦在线观看视频一区| 最新中文字幕久久久久| 麻豆国产97在线/欧美| 亚洲最大成人手机在线| 日韩欧美在线乱码| 最新中文字幕久久久久| 青草久久国产| 国产淫片久久久久久久久 | 国内精品美女久久久久久| 亚州av有码| 无人区码免费观看不卡| 欧美日本视频| 伊人久久精品亚洲午夜| 国产一区二区在线av高清观看| 99热这里只有是精品在线观看 | 日日干狠狠操夜夜爽| 日本免费a在线| 午夜激情欧美在线| av专区在线播放| 亚洲精品成人久久久久久| 嫁个100分男人电影在线观看| 特级一级黄色大片| 亚洲av日韩精品久久久久久密| 真人一进一出gif抽搐免费| 一区二区三区激情视频| 禁无遮挡网站| 韩国av一区二区三区四区| 热99在线观看视频| 欧美中文日本在线观看视频| 久久久久久久亚洲中文字幕 | 亚洲精品成人久久久久久| 最后的刺客免费高清国语| 欧美日韩瑟瑟在线播放| 国产白丝娇喘喷水9色精品| 少妇丰满av| 免费观看人在逋| av黄色大香蕉| 三级国产精品欧美在线观看| 最近视频中文字幕2019在线8| 99精品在免费线老司机午夜| 精品久久久久久久久亚洲 | 亚洲专区中文字幕在线| 美女xxoo啪啪120秒动态图 | 国产成人aa在线观看| 亚洲国产高清在线一区二区三| 亚洲av电影在线进入| 亚洲美女搞黄在线观看 | 狠狠狠狠99中文字幕| 精品久久久久久久久久免费视频| 国产三级中文精品| 亚洲av熟女| 欧美一级a爱片免费观看看| 日本与韩国留学比较| 在线免费观看不下载黄p国产 | 欧美区成人在线视频| 午夜老司机福利剧场| 女同久久另类99精品国产91| 欧美性感艳星| 少妇的逼水好多| 久久精品影院6| www.熟女人妻精品国产| 自拍偷自拍亚洲精品老妇| bbb黄色大片| 亚洲精品在线美女| 亚洲精华国产精华精| 757午夜福利合集在线观看| 观看美女的网站| 91九色精品人成在线观看| 99久久精品热视频| 国内精品久久久久久久电影| 亚洲无线观看免费| 搡女人真爽免费视频火全软件 | 一个人免费在线观看的高清视频| 国产探花在线观看一区二区| 中文字幕人成人乱码亚洲影| 久9热在线精品视频| 赤兔流量卡办理| 97人妻精品一区二区三区麻豆| 美女xxoo啪啪120秒动态图 | 欧美在线黄色| 毛片女人毛片| 小说图片视频综合网站| 欧美性猛交黑人性爽| 99热精品在线国产| 村上凉子中文字幕在线| 国产精品女同一区二区软件 | 婷婷精品国产亚洲av在线| 国产亚洲欧美在线一区二区| 色综合欧美亚洲国产小说| 99视频精品全部免费 在线| 国产黄色小视频在线观看| 中文字幕久久专区| 亚洲真实伦在线观看| 久久亚洲真实| 怎么达到女性高潮| 亚洲激情在线av| 国产日本99.免费观看| 国产乱人伦免费视频| 亚洲国产欧洲综合997久久,| 亚洲人与动物交配视频| 国内少妇人妻偷人精品xxx网站| 一区二区三区四区激情视频 | 午夜福利欧美成人| 男女视频在线观看网站免费| 国产激情偷乱视频一区二区| 少妇丰满av| 日本 欧美在线| 国产色婷婷99| 久久人人精品亚洲av| 免费人成视频x8x8入口观看| 亚洲人成网站高清观看| 亚洲自拍偷在线| 99在线人妻在线中文字幕| 俺也久久电影网| 成人国产综合亚洲| 亚洲av成人av| 能在线免费观看的黄片| 首页视频小说图片口味搜索| 午夜福利在线观看免费完整高清在 | 久久香蕉精品热| 淫秽高清视频在线观看| 老师上课跳d突然被开到最大视频 久久午夜综合久久蜜桃 | 精品国产亚洲在线| av在线天堂中文字幕| 久久草成人影院| 亚洲精品在线美女| 国产高清有码在线观看视频| 亚洲av五月六月丁香网| 日本精品一区二区三区蜜桃| 蜜桃亚洲精品一区二区三区| 国产精品久久久久久亚洲av鲁大| 免费人成视频x8x8入口观看| 最近最新中文字幕大全电影3| 十八禁网站免费在线| 如何舔出高潮| 日韩免费av在线播放| 色精品久久人妻99蜜桃| 亚洲 欧美 日韩 在线 免费| 女人十人毛片免费观看3o分钟| av专区在线播放| 亚洲精品一区av在线观看| 色av中文字幕| 日韩人妻高清精品专区| 日日干狠狠操夜夜爽| 在线a可以看的网站| 51午夜福利影视在线观看| 天堂av国产一区二区熟女人妻| 国产午夜精品久久久久久一区二区三区 | av在线老鸭窝| 91午夜精品亚洲一区二区三区 | 国产精品亚洲一级av第二区| 舔av片在线| 国产精品人妻久久久久久| 日韩欧美免费精品| 国产精品一区二区免费欧美|