Modeling and forecasting hotel room demand based on advance booking information

Lee, Misuk

doi:10.1016/j.tourman.2017.11.004

Cited by 43 publications

(36 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…An example can be found in [7] where past data are averaged to produce individual booking curves for each season. The models proposed in [8] exploit dependence of late and early bookings by partitioning booking horizon to a number of intervals, and application of negative multinomial model. While booking curve application seems to be attractive, on the other hand it is vulnerable to overfitting, which may be the reason of the common use of simpler models similar to mentioned above [6].…”

Section: Related Workmentioning

confidence: 99%

“…where C is capacity, β rate coefficient and m offset parameter. The implemented model extends (8) in that the capacity and rate may be changed in user defined moments to adjust to varying external conditions. The seasonality component s(t) is even more complex as it consists of a set of harmonic functions, namely truncated Fourier series.…”

Section: A Time Series Forecasting With Prophetmentioning

confidence: 99%

See 1 more Smart Citation

Improving Time-Series Demand Modeling in Hospitality Business by Analytics of Public Event Datasets

Кamola

Arabas

2020

IEEE Access

View full text Add to dashboard Cite

Forecasting occupancy in hospitality business with autoregressive time-series models does not intercept occasional impact of public events. Our goal was to find appropriate datasets and enrich existing predictive models to account for rare and explicable demand surges. The paper proposes processing framework: data source types and formats, and forecast algorithms based on natural language processing. The study shows that classical models using word collocations outperform state of the art deep neural networks. Also, the collocations that turn out to be important, occupy certain locations in a graph that represents the natural language. The findings may result in yet improved forecasts, leading to smarter offer pricing and, finally, increased competitiveness in hospitality business. They may also serve public interest in areas like parking management or public transport planning. INDEX TERMS Time series analysis, recurrent neural networks, regression analysis, natural language processing, predictive models.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: A Time Series Forecasting With Prophetmentioning

confidence: 99%

Improving Time-Series Demand Modeling in Hospitality Business by Analytics of Public Event Datasets

Кamola

Arabas

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The academic literature contains a sizable body of hotel forecasting studies. Chen and Kachani (2007) combine exponential smoothing and advance booking models; whereas, Lee (2018) constructs a non-homogeneous Poisson process that incorporates information such as early and late booking patterns. In addition, in recent years, various machine learning approaches have been applied to hotel forecasting (Zhang, 2018).…”

Section: Ihrmentioning

confidence: 99%

The increased importance of competitor benchmarking as a strategic management tool during COVID-19 recovery

Magnini

Crotts

Calvert

2020

IHR

View full text Add to dashboard Cite

PurposeWhile all recoveries are good, some are better than others with regard to their speed and/or magnitude. Many revenue-related key performance indicators (KPIs), such as comparisons to budgets and forecasts that were designed pre-pandemic to assess a hotel's or destination's performance are no longer valid. Therefore, the primary purpose of this conceptual paper is to highlight the need to peg financial-related KPIs relative to competitors' performance during and following a radical market disruption. The secondary purpose of this paper is to summarize advances reported in the literature and in the industry related to competitor benchmarking and accurately identifying competitor sets.Design/methodology/approachThis conceptual paper synthesizes research from disparate sources to offer a series of recommendations to the industry regarding best practices for developing and monitoring revenue-related KPIs during pandemic recovery. Such KPIs will be different based upon hospitality or tourism sector but must be largely founded upon benchmarking off comparable operations.FindingsIndustry disruptions triggered by COVID-19 underscore the need (1) to increasingly utilize competitor-based revenue KPI benchmarks; (2) to have reliable competitor benchmarking data more readily available for use by hotels and destination marketing organizations (DMOs) and (3) for both hotels and DMOs to more accurately identify their competitive sets.Originality/valueThe recommendations offered in this paper are anchored with appropriate theories and empirical research; and as a consequence, offer guidance for the industry for KPI formulation during and following the pandemic.

show abstract

“…Despite the importance of forecasting hotel demand, the literature on the topic is scarce and does not take advantage of technological and scientific methods. The existing literature shows three types of methods: time series, advance booking, and combined modelling (Lee, 2018). However, these methods are parametric and assume that data has a known distribution and that it is possible to estimate the needed parameters (Talluri & Van Ryzin, 2005).…”

Section: Revenue Management and Forecasting Modelsmentioning

confidence: 99%

Predictive models for hotel booking cancellation: a semi-automated analysis of the literature

António

Almeida²,

Nunes³

2019

TMS

View full text Add to dashboard Cite

This study sought to combine data science tools and capabilities with human judgement and interpretation in order to demonstrate how semiautomatic analysis of the literature can contribute to identifying and synthesising research findings and topics about booking cancellation forecasting. The study also focused on recording in detail the analysis's full experimental procedure to encourage other researchers to conduct automated literature reviews in order to understand more fully the current tendencies in their field of study. The data were obtained through a keyword search in Scopus and Web of Science databases. The methodology presented not only diminishes human bias but also enhances data visualisation and text mining techniques' ability to facilitate abstraction, expedite analysis and improve literature reviews. The results show that, despite the importance of forecasting booking cancellations to understanding net demand and improving cancellation and overbooking policies, further research on this subject is needed.

show abstract

Modeling and forecasting hotel room demand based on advance booking information

Cited by 43 publications

References 14 publications

Improving Time-Series Demand Modeling in Hospitality Business by Analytics of Public Event Datasets

Improving Time-Series Demand Modeling in Hospitality Business by Analytics of Public Event Datasets

The increased importance of competitor benchmarking as a strategic management tool during COVID-19 recovery

Predictive models for hotel booking cancellation: a semi-automated analysis of the literature

Contact Info

Product

Resources

About