Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

Aliyev, Rashad R; Salehi, Sara; Aliyev, Rafig R.

doi:10.3390/su11030793

Cited by 23 publications

(14 citation statements)

References 29 publications

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“…Previous studies forecasted the demands of tourist hotels mainly based on linear models [15][16][17][18][19]. For example, Choi [18] identified key economic indicators of the hotel industry in the US and built synthetic indicators to forecast the US hotel demands successfully.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Choi [18] identified key economic indicators of the hotel industry in the US and built synthetic indicators to forecast the US hotel demands successfully. Aliyev et al [19] used fuzzy time series models to forecast the hotel occupancy. There was very rare research specializing in the hotel demand forecasting using the Internet search data.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Zhang

Wang

et al. 2019

Sustainability

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Accurate forecasting of the hotel accommodation demands is extremely critical to the sustainable development of tourism-related industries. In view of the ever-increasing tourism data, this paper constructs a deep learning framework to handle the prediction problem in the hotel accommodation demands. Taking China's Hainan province as an empirical example, the internet search index is used from August 2008 to May 2019 to forecast the overnight passenger flows for hotels accommodation in Hainan Province, China. Forecasting results indicate that compared to benchmark models, the constructed forecasting method can effectively simulate dynamic characteristics of the overnight passenger flows for the hotel accommodation and significantly improve the forecasting performance of the model. Forecasting results can provide necessary references for decision-making in tourism-related industries, and this forecasting framework can also be extended to other similar complex time series forecasting problems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Zhang

Wang

et al. 2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…In the hospitality, accurate forecasting is the key to revenue management and other related strategies. Time series, advance booking and combined model are three methods commonly used in hotel demand forecasting [8,11,12,13]. This paper is particularly interested in time series forecasting models, and will focus on the researches related to this method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Some use variables related to financial performance to measure demand, such as using statistical residuals to predict revenue per room [3]; using autoregressive integrated moving average and intervention analysis technique to forecast hotel performance [4]. Some also use variables related to the demand scale, such as introducing the bank exchange rate index into the hotel standard demand equation to predict the number of rooms sold [5]; introducing business sentiment indicators to predict the actual number of hotel arrivals [6]; forecasting the number of overnight based on search engine data and LSTM model [7]; using fuzzy c-means clustering algorithm to forecasting occupancy rate [8]. No matter from which point of view, forecasting hotel demand is essentially based on the analysis of data.…”

Section: Introductionmentioning

confidence: 99%

A K-means Group Division and LSTM Based Method for Hotel Demand Forecasting

Wang

2021

Teh. vjesn.

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The accuracy of hotel demand forecasting is affected by factors such as the completeness of historical data and the maturity of models. Most of the existing methods are based on rich data, without considering that single hotels may only obtain sparse data. Therefore, a K-means group division and Long Short-Term Memory (LSTM) based method is proposed in this paper. Guest types are introduced into the forecasting to provide reference for hotel's further decision-making. Using an example of 1493 hotels in Europe, we divide hotel groups and forecast the flow of leisure and business guests. The experimental results show that, compared with the benchmark models, LSTM can improve the forecasting performance of hotel group; compared with single hotels, the forecasting of hotel groups can effectively avoid inaccuracy caused by sparse data. The results can provide necessary reference for hospitality to make decisions based on guest types.

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“…These cash-flow deficits are random variables by nature that depend on the company's economic performance and are detected in this paper using the Mamdani fuzzy logic system [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

The Development of a Fuzzy Logic System in a Stochastic Environment with Normal Distribution Variables for Cash Flow Deficit Detection in Corporate Loan Policy

2019

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This paper develops a Mamdani fuzzy logic system (FLS) that has stochastic fuzzy input variables designed to identify cash-flow deficits in bank lending policies. These deficits do not cover the available cash-flow (CFA) resulting from the company's operating activity. Thus, due to these deficits, solutions must be identified to avoid companies' financial difficulties. The novelty of this paper lies in its using stochastic fuzzy variables, or those categories of variables that are defined by fuzzy sets, characterized by normally distributed density functions specific to random variables, and characterized by fuzzy membership functions. The variation intervals of the stochastic fuzzy variables allow identification of the probabilistic risk situations to which the company is exposed during the crediting period using the Mamdani-type fuzzy logic system. The mechanism of implementing the fuzzy logic system is based on two stages. The first is based on the determination of the cash-flow requirements resulting from loan reimbursement and interest rates. This stage has the role of determining the need for financial resources to cover the liabilities. The second stage is based on the identification of the stochastic fuzzy variables which have a role in influencing the cash flow deficits and the probability values estimation of these variables taking into account probability calculations. Based on these probabilistic values, using the Mamdani fuzzy logic system, estimations are computed for the available cash-flow (the output variable). The estimated values for CFA are then used to detect probability risk situations in which the company will not have enough resources to cover its liabilities to financial creditors. All the FLS calculations refer to future time periods. Testing and simulating the fuzzy controller confirms its functionality.

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Development of Fuzzy Time Series Model for Hotel Occupancy Forecasting

Cited by 23 publications

References 29 publications

Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index

A K-means Group Division and LSTM Based Method for Hotel Demand Forecasting

The Development of a Fuzzy Logic System in a Stochastic Environment with Normal Distribution Variables for Cash Flow Deficit Detection in Corporate Loan Policy

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