Forecasting hotel reservations with long short-term memory-based recurrent neural networks

Wang, Jian; Duggasani, Amar

doi:10.1007/s41060-018-0162-6

Cited by 15 publications

(16 citation statements)

References 25 publications

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“…Consequently, Schwartz et al (2016) and Huang and Zheng (2021) made innovative attempts to incorporate the demand of other hotels as explanatory variables in their forecasting models. Furthermore, given that the hotel’s demand can be affected by the pricing strategy of other hotels in the same region (Wang and Duggasani, 2020), Assaf and Tsionas (2019) and Guizzardi et al (2020) used the demand and price of other hotels to enhance the forecast accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Daily hotel demand forecasting with spatiotemporal features

Huang

Zheng

2022

IJCHM

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Purpose Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region. Design/methodology/approach For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units. Findings The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period). Practical implications From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region. Originality/value The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

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Section: Introductionmentioning

confidence: 99%

“…The possible reasons are twofold. First, hotel demand is influenced by many complex and interrelated factors (Huang and Zheng, 2021; Wang and Duggasani, 2020; Zheng et al , 2020). Second, hotel demand emphasizes short-term forecasting (e.g.…”

Section: Introductionmentioning

confidence: 99%

Daily hotel demand forecasting with spatiotemporal features

Huang

Zheng

2022

IJCHM

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“…The hotel and travel sector is derived significant benefits from the widespread use of AI. This study [36] proposed three types of forecasting hotel occupancy methods with the concepts of neural networks and proposed two long short-term memory (LSTM) models based on recurrent neural networks. To measure the relative performance, six ML models of the decision tree, multilayer perceptron, lasso, linear regression, random forest, and ridge are also estimated and tested against the same datasets.…”

Section: Opportunities Of Ai Across Industriesmentioning

confidence: 99%

The Artificial Intelligence (AI) Model Canvas Framework and Use Cases

Nurcahyo

Suroso

Wang

2022

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

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Artificial Intelligence (AI) has grown increasingly in the past decade. The growth and development bring up several issues for a successful AI project. The AI project requires communication across different domains, like specialists, engineers, data scientists, stakeholders, and ecosystem partners (analytic, storage, labeling, and open-source platforms). It offers numerous vital qualities to give deeper insights into user behavior and give recommendations based on the data. The AI project is hard to define, it requires more than mastery of data, and every enterprise needs guidance and a simple plan on how to use AI. This research creates a wide-view approach of different types of AI Model Canvas for companies that do projects, produce, promote and provide AI technology to organizations. We selected three canvases that represented AI, Machine Learning (ML), and Deep Learning (DL) method. We illustrate and interpret those canvas along with some case studies. We conclude our research by writing the final case report for each use case from the AI model canvas. By filling the one-page Canvas, it will help us explain what AI will provide, how it will interact with humans judgment, and how it will be used to influence decisions, how you will measure success & outcome, and the type of data needed to train, operate, and improve AI. The AI Model Canvas purposed a clear description and differentiation of the roles of stakeholders, customers, and AI strategy. This canvas also can be used in analytical and assembly projects in making new product lines.

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“…The models considered mimic churn reduction techniques used in e.g. telecommunications and include linear and nonlinear classifiers [10] with neural networks becoming popular choice [11], [12], [13],…”

Section: Related Workmentioning

confidence: 99%

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

Кamola

Arabas

2020

IEEE Access

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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.

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Forecasting hotel reservations with long short-term memory-based recurrent neural networks

Cited by 15 publications

References 25 publications

Daily hotel demand forecasting with spatiotemporal features

Daily hotel demand forecasting with spatiotemporal features

The Artificial Intelligence (AI) Model Canvas Framework and Use Cases

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

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