ChatGPT预测旅游需求的准确度如何?

IF 10.9 1区 管理学 Q1 ENVIRONMENTAL STUDIES Tourism Management Pub Date : 2024-12-29 DOI:10.1016/j.tourman.2024.105119
Doris Chenguang Wu , Wenjia Li , Ji Wu , Mingming Hu , Shujie Shen
{"title":"ChatGPT预测旅游需求的准确度如何?","authors":"Doris Chenguang Wu ,&nbsp;Wenjia Li ,&nbsp;Ji Wu ,&nbsp;Mingming Hu ,&nbsp;Shujie Shen","doi":"10.1016/j.tourman.2024.105119","DOIUrl":null,"url":null,"abstract":"<div><div>ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for forecasting tourism demand from temporal data, specifically historical tourism arrivals data, remains an unexplored frontier. This research presents the first attempt to conduct an extensive Zero-shot and Chain-of-Thought analysis of ChatGPT's capabilities in tourism demand forecasting, under various temporal scenarios. Based on the Macau inbound tourism arrivals dataset, our empirical findings indicate that the predictive capability of ChatGPT-4 is noteworthy compared to the three benchmark time series models (Naïve, Exponential Smoothing, SARIMA) and the three benchmark machine learning models (Random Forest, Multi-Layer Perceptron, Long Short-Term Memory), especially when the forecast horizon is relatively short. Furthermore, compared to Zero-shot prompts, engaging in continuous dialogue can enhance the forecast accuracy of ChatGPT-4. This performance of ChatGPT highlights its potential for quantitative data prediction as a new user-friendly and cost-effective management tool.</div></div>","PeriodicalId":48469,"journal":{"name":"Tourism Management","volume":"108 ","pages":"Article 105119"},"PeriodicalIF":10.9000,"publicationDate":"2024-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How well can ChatGPT forecast tourism demand?\",\"authors\":\"Doris Chenguang Wu ,&nbsp;Wenjia Li ,&nbsp;Ji Wu ,&nbsp;Mingming Hu ,&nbsp;Shujie Shen\",\"doi\":\"10.1016/j.tourman.2024.105119\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for forecasting tourism demand from temporal data, specifically historical tourism arrivals data, remains an unexplored frontier. This research presents the first attempt to conduct an extensive Zero-shot and Chain-of-Thought analysis of ChatGPT's capabilities in tourism demand forecasting, under various temporal scenarios. Based on the Macau inbound tourism arrivals dataset, our empirical findings indicate that the predictive capability of ChatGPT-4 is noteworthy compared to the three benchmark time series models (Naïve, Exponential Smoothing, SARIMA) and the three benchmark machine learning models (Random Forest, Multi-Layer Perceptron, Long Short-Term Memory), especially when the forecast horizon is relatively short. Furthermore, compared to Zero-shot prompts, engaging in continuous dialogue can enhance the forecast accuracy of ChatGPT-4. This performance of ChatGPT highlights its potential for quantitative data prediction as a new user-friendly and cost-effective management tool.</div></div>\",\"PeriodicalId\":48469,\"journal\":{\"name\":\"Tourism Management\",\"volume\":\"108 \",\"pages\":\"Article 105119\"},\"PeriodicalIF\":10.9000,\"publicationDate\":\"2024-12-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tourism Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0261517724002383\",\"RegionNum\":1,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tourism Management","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0261517724002383","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0

摘要

ChatGPT已经在各种自然语言处理(NLP)任务中展示了非凡的能力。然而,它从时间数据预测旅游需求的潜力,特别是历史旅游人数数据,仍然是一个未开发的前沿。本研究首次尝试对ChatGPT在各种时间情景下的旅游需求预测能力进行广泛的零射击和思维链分析。基于澳门入境旅游人数数据,我们的实证研究结果表明,与三个基准时间序列模型(Naïve,指数平滑,SARIMA)和三个基准机器学习模型(随机森林,多层感知器,长短期记忆)相比,ChatGPT-4的预测能力值得注意,特别是当预测范围相对较短时。此外,与Zero-shot提示相比,进行连续对话可以提高ChatGPT-4的预测精度。ChatGPT的这一性能突出了其作为一种新的用户友好和成本效益管理工具的定量数据预测的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
How well can ChatGPT forecast tourism demand?
ChatGPT has demonstrated remarkable capabilities across various natural language processing (NLP) tasks. However, its potential for forecasting tourism demand from temporal data, specifically historical tourism arrivals data, remains an unexplored frontier. This research presents the first attempt to conduct an extensive Zero-shot and Chain-of-Thought analysis of ChatGPT's capabilities in tourism demand forecasting, under various temporal scenarios. Based on the Macau inbound tourism arrivals dataset, our empirical findings indicate that the predictive capability of ChatGPT-4 is noteworthy compared to the three benchmark time series models (Naïve, Exponential Smoothing, SARIMA) and the three benchmark machine learning models (Random Forest, Multi-Layer Perceptron, Long Short-Term Memory), especially when the forecast horizon is relatively short. Furthermore, compared to Zero-shot prompts, engaging in continuous dialogue can enhance the forecast accuracy of ChatGPT-4. This performance of ChatGPT highlights its potential for quantitative data prediction as a new user-friendly and cost-effective management tool.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Tourism Management
Tourism Management Multiple-
CiteScore
24.10
自引率
7.90%
发文量
190
审稿时长
45 days
期刊介绍: Tourism Management, the preeminent scholarly journal, concentrates on the comprehensive management aspects, encompassing planning and policy, within the realm of travel and tourism. Adopting an interdisciplinary perspective, the journal delves into international, national, and regional tourism, addressing various management challenges. Its content mirrors this integrative approach, featuring primary research articles, progress in tourism research, case studies, research notes, discussions on current issues, and book reviews. Emphasizing scholarly rigor, all published papers are expected to contribute to theoretical and/or methodological advancements while offering specific insights relevant to tourism management and policy.
期刊最新文献
Exploring the cultural influence on tourists’ color perceptions: A study of tourist photography Shaping safety: Effective signage for tourist attractions Mapping the landscape of employer value propositions in Asian hotels through online job postings analysis Virtual reality tourism as a therapeutic tool: Assessing the well-being benefits of repeated restorative environment exposures for individuals with GAD Decoding the cultural heritage tourism landscape and visitor crowding behavior from the multidimensional embodied perspective: Insights from Chinese classical gardens
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1