- Project Owner 👉 : Yishuo Zhang
- Prepared by 🌷 TULIP Lab
Tourism demand forecasting is an important and challenging task in the tourism industry, whose goal is to forecast the number of tourists arriving at a destination in a certain period. Accurate tourism demand forecasting can help policymakers and practitioners to make appropriate strategies and plans, which is beneficial for promoting local development. Many studies focus on developing diverse models for tourism demand to improve the prediction ability, such as time-series models, econometric models, and artificial intelligence (AI) models. In general, tourism demand forecasting is defined as the task of predicting or estimating the number of tourists arriving at the target destination in a certain period of time.
Despite numerous advancements in the three modeling categories of tourism forecasting literature, several crucial methodological problems still require further attention. For example, the common key challenges on tourism demand forecasting is on how to improve accuracy and omit overfitting of the developed model. Another important aspect focused by researchers recently is to explore the pattern between data characteristics and theoretical forecasting performance. With all above research challenges ahead, this project aims to explore and implement the most recent studies regarding to the abovementioned challenges, benchmark, discuss and review those solutions to find promising research directions on tourism demand forecasting.
📔 NEXUS-S1 (compatible with SIT723)
The related documents are encrypted, and you will receive the password upon the acceptance into stage 1️⃣ of the project.
- TO BE ADDED
📔 NEXUS-S2 (compatible with SIT724)
The related documents are encrypted, and you will receive the password upon the acceptance into the stage 2️⃣ of the project.
- TO BE ADDED
📔 NEXUS-S3 (compatible with Honours)
The related documents are encrypted, and you will receive the password upon the acceptance into the stage 3️⃣ of the project.
- TO BE ADDED