ABSTRACT
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.
ABSTRACT
OBJECTIVE: Isatidis Radix is the dried root of Isatis indigotica Fortune of Cruciferae. As a representative traditional Chinese medicine for heat-clearing and detoxification, Isatidis Radix and its preparations are widely used in the prevention and treatment of all kinds of colds and have played an active role in the prevention and treatment of SARS, H1N1 and COVID-19. Although the chemical ingredient of Isatidis Radix has been studied deeply, there is no information bank, website or literature that can comprehensively query the information of all compounds at home and abroad, which is not conducive to the development of related research. So establishment of the chemical composition information bank is in need. METHODS: According to the category of chemical ingredients, the Chinese and English names, molecular formulas, exact molecular weights, structural formulas and references of nearly 400 chemical components in Isatidis Radix were comprehensively sorted out, and the chemical composition information bank of Isatidis Radix was constructed. RESULTS: By September 2020, a total of 392 compounds in 17 categories had been extracted, isolated and identified from Isatidis Radix. CONCLUSIONT: The established chemical composition information bank can provide the basis for the separation and identification of chemical components, quality control, material basis mining, network pharmacology research and so on. Copyright 2022 by the Chinese Pharmaceutical Association.