Tourist arrival forecasting with game-theoretic social influence weighting and deep learning

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Video file (mp4)

The gist

The gist The proposed framework integrates game theory-based opinion leader detection, Empirical Wavelet Transform, and a Stacked BiLSTM network to generate accurate demand forecasts for

In short

The study developed a hybrid forecasting method for tourist arrivals in Hong Kong and Sanya. It combines game theory to identify influential social media leaders, Empirical Wavelet Transform to handle complex time-series data, and a Stacked BiLSTM network tuned by Bayesian Optimization. The results show this approach significantly outperforms existing models by effectively integrating big data and opinion leader insights.

Key concepts

Game-Theoretic Opinion Leader Detection
This method uses game theory to find groups of social media users who have the maximum combined influence, or 'synergy.' It calculates a 'Trust Score' for users based on various trust metrics. The Shapley value is then used to precisely determine the contribution and synergy of each leader in a coalition, allowing the model to weigh influential opinions more heavily.
Empirical Wavelet Transform (EWT)
EWT is a mathematical tool used to analyze time-series data by breaking it down into different frequency components. This helps manage nonstationary attributes like trends and seasonality in tourist arrival data. By applying EWT, the model can effectively filter out cyclical patterns and extract meaningful features from the complex historical data.
Stacked Bi-directional Long Short-Term Memory (Stacked BiLSTM)
This is a deep learning architecture designed for sequence prediction. A standard LSTM processes information in one direction, while a Stacked BiLSTM uses two layers of LSTMs, processing data both forwards and backward. This structure allows the model to capture long-term dependencies and complex nonstationary features in the tourist arrival time series.
Bayesian Optimization (BO)
Bayesian Optimization is an intelligent search technique used to tune the hyperparameters of the Stacked BiLSTM network. Instead of randomly testing settings, BO intelligently chooses which hyperparameter combinations to test next based on previous results. This significantly reduces the time needed to find the best model configuration for accurate forecasting.

Terminology used across episodes

This episode discusses

The paper

Tourist arrival forecasting with game-theoretic social influence weighting and deep learning · Read on arXiv

Georgia Institute of Technology

In the era of Industry 5.0, data-driven decision-making has become indispensable for planning in service systems such as tourism. This paper proposes a non-linear hybrid approach for forecasting tourist arrivals to Hong Kong from five major source markets before COVID-19 and to Sanya, China, through the COVID-19 shock. The method integrates multiple sources of Internet big data and employs a cooperative game-theoretic algorithm to identify coalitions of opinion leaders on social media, whose content then receives larger weights. The Empirical Wavelet Transform (EWT), applied causally, manages nonstationary attributes, and a Stacked Bi-directional Long Short-Term Memory (Stacked BiLSTM) network tuned by Bayesian optimization generates the forecasts. We prove that the Shapley values of the underlying trust-weighted coverage game have a closed form and that the coalition of maximal synergy can be found exactly in polynomial time. Relative to the same network fed only with past arrivals, Internet big data reduce the one-step-ahead root mean squared error by 4.1-41.7% across the Hong Kong markets and opinion-leader weighting by a further 10.1-49.4%, against 5.3% and 8.7% for Sanya. The approach compares favorably with published benchmarks and remains robust under volatile conditions, which suggests uses in logistics, supply chains and production planning.

Transcript

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom.

Jane: And I'm Jane, here with Lu, Meng and our in-house large language model, Lalam.

Tom: Today's paper: "Tourist arrival forecasting with game-theoretic social influence weighting and deep learning".

Jane: The gist The proposed framework integrates game theory-based opinion leader detection, Empirical Wavelet Transform,

Tom: First, who's behind it and why it matters.

Title and authors: Tom: Moving into the specific details of "Tourist arrival forecasting with game-theoretic social influence weighting and deep learning," Jane, what are we looking at regarding the authors? Who came up with this work?

Jane: The paper is written by Ali Nikseresht from the School of Computer Science at Georgia Institute of Technology in Atlanta, and it's a collaboration.

Lu: Nikseresht is a senior researcher at Tsinghua University, and his team has been looking into representation learning for large language models. So you can tell he’s deep in the AI stuff.

Meng: He’s doing work on how to get these models to actually perform well when deployed, so I assume this paper is focused on making a model that works in the real world, not just a theoretical exercise.

Tom: Right. The abstract mentions it addresses forecasting international tourist arrivals in two specific contexts: Hong Kong from five major source nations pre-COVID-nineteen and Sanya in Hainan province post-COVID-nineteen <ref:2507.03411#pg1,Hong Kong from five major source nations pre-COVID-19, and>. That’s a big scope for one model to handle.

Jane: It really is; they are tackling two different types of tourism demand problems—pre and post pandemic scenarios—which makes the application quite diverse.

Lu: The paper highlights that they are using multiple sources of Internet big data, and specifically points out a gap in the literature where few papers have considered a mixture of multi-Internet big data in tourism demand forecasting twenty-six <ref:2507.03411#pg3,papers have considered a mixture of multi-Internet big data in tourism>.

Tom: That’s a key contribution, right? They are explicitly stating that using this mixture is the first major thing they contribute.

Jane: It suggests that just looking at volume or just valence isn't enough; you need to combine them.

Meng: I see why. If we only look at one signal, we’re probably missing half the picture of what a tourist is doing online.

The paper's summary: Tom: So, if we distill the abstract down to its simplest terms, what’s the actual problem they are solving in this paper?

Jane: They are trying to solve the problem of accurately forecasting how many international tourists will show up at a destination. It’s complicated because demand isn't simple; it has lots of non-linear dependencies, volatility, and seasonality that traditional models struggle with.

Lu: Traditional econometric models just can't keep up with all that complexity when you add in the noise from social media data and other big data sources.

Tom: And the solution they propose is a hybrid approach. They combine game theory for finding influencers, EWT to handle time patterns, and a stacked Bidirectional Long Short-Term Memory network for the final prediction.

Jane: So it’s about making the prediction more robust by layering these different techniques on top of each other to capture different parts of the demand signal that single models miss.

Meng: It sounds like a lot of components, which makes me wonder how much data they actually need to train all this up without running into massive computational issues.

Tom: They address that with the optimization part where they use Bayesian Optimization to tune the hyperparameters of their stacked BiLSTM network, which helps manage the complexity.

Jane: It’s a clever way to handle the nonstationary nature of tourism demand data, which keeps the model from becoming stale too quickly.

The paper's improvements: Tom: Let's focus on what they specifically claim are the improvements over what came before in this area. What makes this approach better?

Lu: Their first major contribution is utilizing a mixture of multi-Internet big data sources, considering both volume and valence features, which enhances decision-making efficiency.

Jane: And they also introduce the game theory based algorithm to identify opinion leaders using the Shapley value to calculate synergy. That’s a structured way to pinpoint influential communities instead of just looking at everything.

Tom: And then they layer in the Empirical Wavelet Transform, which handles cyclical, seasonal, and trend features in a way that gives them refined time-frequency analysis.

Meng: I’m interested in how this helps with deployment—does this reduce the computational load or just make it more complex to run?

Tom: The stacked Bidirectional Long Short-Term Memory network is designed to deal with nonstationary features, so the model itself is structured to handle those changing patterns better.

Jane: And they show that incorporating these elements together leads them to achieve lower error metrics compared to state-of-the-art models across various scenarios.

Conclusion: Tom: So, to wrap up the paper "Tourist arrival forecasting with game-theoretic social influence weighting and deep learning," what’s the final message they are trying to send?

Jane: The final message is that combining multi-Internet big data, game theory for opinion leader detection, EWT for time-frequency analysis, and a memory-aware stacked BiLSTM network creates a more accurate forecasting framework.

Lu: It suggests that this hybrid approach is effective when you look at the accuracy improvements across different statistical tests and scenarios for both Hong Kong and Sanya demand forecasts.

Meng: From an operational standpoint, it means businesses can use these kinds of data-driven methods to pinpoint sectors with the most significant impact on their specific demand fluctuations.

Jane: For policymakers, it suggests they should rely on these accurate predictions to plan infrastructure and resource allocation for places like Hong Kong and Sanya.

Tom: It’s about using this paper as a blueprint for building more sophisticated, data-driven systems in the tourism industry.

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