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This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

This paper intends to construct a new adaptive grey seasonal model (AGSM) to promote the application of the grey forecasting model in quarterly GDP.

Firstly, this paper constructs a new accumulation operation that embodies the new information priority by using a hyperparameter. Then, a new AGSM is constructed by using a new grey action quantity, nonlinear Bernoulli operator, discretization operation, moving average trend elimination method and the proposed new accumulation operation. Subsequently, the marine predators algorithm is used to quickly obtain the hyperparameters used to build the AGSM. Finally, comparative analysis experiments and ablation experiments based on China's quarterly GDP confirm the validity of the proposed model.

AGSM can be degraded to some classical grey prediction models by replacing its own structural parameters. The proposed accumulation operation satisfies the new information priority rule. In the comparative analysis experiments, AGSM shows better prediction performance than other competitive algorithms, and the proposed accumulation operation is also better than the existing accumulation operations. Ablation experiments show that each component in the AGSM is effective in enhancing the predictive performance of the model.

A new AGSM with new information priority accumulation operation is proposed.

A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

Online reviews serve as valuable sources of information, reflecting tourists’ attentions, preferences and sentiments. However, although the existing research has demonstrated that incorporating online review data can enhance the performance of tourism demand forecasting models, the reliability of online review data and consumers’ decision-making process have not been given adequate attention. To address the aforementioned problem, the purpose of this study is to forecast tourism demand using online review data derived from the analysis of review helpfulness.

The authors propose a novel “identification-first, forecasting-second” framework. This framework prioritizes the identification of helpful reviews through a comprehensive analysis of review helpfulness, followed by the integration of helpful online review data into the forecasting system. Using the SARIMAX model with helpful online review data sourced from TripAdvisor, this study forecasts tourist arrivals in Hong Kong during the period from August 2012 to June 2019. The SNAÏVE/SARIMA model was used as the benchmark model. Additionally, artificial intelligence models including long short-term memory, back propagation neural network, extreme learning machine and random forest models were used to assess the robustness of the results.

The results demonstrate that online review data are subject to noise and bias, which can adversely affect the accuracy of predictions when used directly. However, by identifying helpful online reviews beforehand and incorporating them into the forecasting process, a notable enhancement in predictive performance can be realized.

First, to the best of the authors’ knowledge, this study is one of the first to focus on the data issue of online reviews on tourism arrivals forecasting. Second, this study pioneers the integration of the consumer decision-making process into the domain of tourism demand forecasting, marking one of the earliest endeavors in this area. Third, this study makes a novel attempt to identify helpful online reviews based on reviews helpfulness analysis.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system.

First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research.

Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance.

Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem.

The highlights of the paper are as follows:

1. A new seasonal grey buffer operator is constructed.

2. The impact of shock perturbations on seasonal data trends is effectively mitigated.

3. A novel seasonal grey forecasting approach with multi-method fusion is proposed.

4. Seasonal electricity consumption is successfully predicted by the novel approach.

5. The way to adjust China's power system flexibility in the future is analyzed.

A new seasonal grey buffer operator is constructed.

The impact of shock perturbations on seasonal data trends is effectively mitigated.

A novel seasonal grey forecasting approach with multi-method fusion is proposed.

Seasonal electricity consumption is successfully predicted by the novel approach.

The way to adjust China's power system flexibility in the future is analyzed.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

The logistics industry has undergone a tremendous transformation. This transformation is necessary to cope with the fundamental changes in customer expectations and the need for digitalization imposed by the pandemic, changes in the socioeconomic world, and innovative technology solutions. This paper aims to present digital transformation as an integrated framework for transforming the operating model and applying advanced solutions to the ecosystem of a quintile logistics (5PL) company. 5PL operators are typically an ecosystem. Loosely coupled or self-organized entities that collaborate in a symbiotic relationship represent this ecosystem. They aim to jointly develop capabilities, create innovative services or solutions, share knowledge, facilitate transactions, and leverage network synergies in a logistics environment to provide optimized or novel customer- or partner-centric solutions (Lamberjohann and Otto, 2020).

Currently, there is no single definition of an integrated logistics operations model in 5PL practice, so the qualitative method used in this paper allows for investigation from an exploratory perspective. The paper follows a qualitative research methodology, collecting and analyzing data/facts through interviews and visits to subject matter experts, industry practitioners, and academic researchers, combined with an extensive review of academic publications, industry reports, and written and media content from established organizations in the marketplace. This paper follows a qualitative research methodology, as it is an inquiry rather than a statistical study. The qualitative method allows the study of the concepts of phenomena and definitions, their characteristics, and the defining features that serve as the basis (Berg, 2007). It emphasizes generalized interpretation and deeper understanding of concepts, which would be more difficult in quantitative, statistically based research. Fact-finding was conducted in two ways: in-depth interviews with experts from academia, information and communication technology organizations, and key players in the logistics industry; and academic publications, industry reports, and written and media content from established national and international organizations in the market.

The operations model introduced considers six aspects: persons, processes, platforms, partners, protection and preservation. A virtual team approach can support the personal side of the 5PL ecosystem’s digital transformation. Managing a 5PL ecosystem should be based on collaborative planning, forecasting, and replenishment methods (Parsa et al., 2020). A digital platform can support trust among the stakeholders in the ecosystem. A blockchain solution can powerfully support the 5PL ecosystem from partner relationships’ points of view. The implementation of a cybersecurity reference model is important for protection (Bandari, 2023). Reverse logistics and an integrated approach support the preservation of the ecosystem.

While the author has experience applying the different components of the operations model presented, it would be interesting to find a 5PL that would use all the components presented in an integrated way. The operations model presented applies to any similar ecosystem with minor adaptations.

This paper addresses operations models and digital transformation challenges for optimizing 5PL operators. It provides several opportunities and considerations for 5PL operators interested in improving their management and operations to cope with the growing challenges of today’s world.

The competitiveness and long-term performance of 5PL operators depend on selecting and carefully implementing their operations models. This paper emphasizes the importance of using advanced operations models.

The operations model derives from the author’s personal experiences in research and the innovative application of these models to logistics operators (DHL, UPS, Poste Italiane and others). This paper brings together academic and industry perspectives and operations models in an integrated business digital transformation. This paper defines an original optimal operations model for a 5PL operator and can add sustainable value to organizations and society. In doing so, it outlines different solution requirements, the critical success factors and the challenges for solutions and brings logistical performance objectives when implementing a digital business transformation.