ChatGPT预测旅游需求的准确度如何?

IF 10.9 1区 管理学 Q1 ENVIRONMENTAL STUDIES
Doris Chenguang Wu , Wenjia Li , Ji Wu , Mingming Hu , Shujie Shen
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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。
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.
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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.
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