The relative roles of different land-use types in bike-sharing demand: A machine learning-based multiple interpolation fusion method

Sun, Chao; LU, Professor JIAN

doi:10.1016/j.inffus.2023.02.033

Cited by 14 publications

(1 citation statement)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mathematical methods mainly consist of three elements: statistical machine learning, model, strategy, and algorithm. A time series is a classic trend prediction method that assumes future values composed of patterns of current and historical data, and it is applied to constructing a model from historical data and then predict future data in machine learning (Caicedo-Torres & Payares, 2016;Mehmood et al, 2022;Qin et al, 2023;Sun & Lu, 2023). According to Calero-Sanz et al (2022), Jiang et al (2023), and Yang et al (2015), the tourism sector, especially the hotel industry, has adopted machine learning for forecasting room booking and cancellation, demand, prices, and occupancy.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Algorithms to Predict Hotel Occupancy

Kozlovskis,

Liu,

Lace

et al. 2023

Journal of Business Economics and Management

View full text Add to dashboard Cite

The development and availability of information technology and the possibility of deep integration of internal IT systems with external ones gives a powerful opportunity to analyze data online based on external data providers. Recently, machine learning algorithms play a significant role in predicting different processes. This research aims to apply several machine learning algorithms to predict high frequent daily hotel occupancy at a Chinese hotel. Five machine learning models (bagged CART, bagged MARS, XGBoost, random forest, SVM) were optimized and applied for predicting occupancy. All models are compared using different model accuracy measures and with an ARDL model chosen as a benchmark for comparison. It was found that the bagged CART model showed the most relevant results (R2 > 0.50) in all periods, but the model could not beat the traditional ARDL model. Thus, despite the original use of machine learning algorithms in solving regression tasks, the models used in this research could have been more effective than the benchmark model. In addition, the variables’ importance was used to check the hypothesis that the Baidu search index and its components can be used in machine learning models to predict hotel occupancy.

show abstract