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This paper aims to illustrate the potential of high-frequency data for tourism and hospitality analysis, through two research objectives: First, this study describes and test a novel high-frequency forecasting methodology applied on big data characterized by fine-grained time and spatial resolution; Second, this paper elaborates on those estimates’ usefulness for visitors and tourism public and private stakeholders, whose decisions are increasingly focusing on short-time horizons.

This study uses the technical communications between mobile devices and WiFi networks to build a high frequency and precise geolocation of big data. The empirical section compares the forecasting accuracy of several artificial intelligence and time series models.

The results robustly indicate the long short-term memory networks model superiority, both for in-sample and out-of-sample forecasting. Hence, the proposed methodology provides estimates which are remarkably better than making short-time decision considering the current number of residents and visitors (Naïve I model).

A discussion section exemplifies how high-frequency forecasts can be incorporated into tourism information and management tools to improve visitors’ experience and tourism stakeholders’ decision-making. Particularly, the paper details its applicability to managing overtourism and Covid-19 mitigating measures.

High-frequency forecast is new in tourism studies and the discussion sheds light on the relevance of this time horizon for dealing with some current tourism challenges. For many tourism-related issues, what to do next is not anymore what to do tomorrow or the next week.

This research initiates high-frequency forecasting in tourism and hospitality studies. Additionally, we detail several examples of how anticipating urban crowdedness requires high-frequency data and can improve visitors’ experience and public and private decision-making.

The purpose of this study is to forecast the development performance of important economies in a smart city using mixed-frequency data.

This study introduces reverse unrestricted mixed-data sampling (RUMIDAS) to support vector regression (SVR) to develop a novel RUMIDAS-SVR model. The RUMIDAS-SVR model was estimated using a quadratic programming problem. The authors then use the novel RUMIDAS-SVR model to forecast the development performance of all high-tech listed companies, an important sector of the economy reflecting the potential and dynamism of urban economic development in Shanghai using the mixed-frequency consumer price index (CPI) producer price index (PPI), and consumer confidence index (CCI) as predictors.

The empirical results show that the established RUMIDAS-SVR is superior to the competing models with regard to mean absolute error (MAE) and root-mean-squared error (RMSE) and multi-source macroeconomic predictors contribute to the development performance forecast of important economies.

Smart city policy makers should create a favourable macroeconomic environment, such as controlling inflation or stabilising prices for companies within the city, and companies within the important city economic sectors should take initiative to shoulder their responsibility to support the construction of the smart city.

This study contributes to smart city monitoring by proposing and developing a new model, RUMIDAS-SVR, to help the construction of smart cities. It also empirically provides strategic insights for smart city stakeholders.

The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.

Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.

This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.

The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.

This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.

The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.

The algorithmic trading has advanced exponentially and necessitates the evaluation of intraday stock market forecasting on the grounds that any stock market series are foreseen to follow the random walk hypothesis. The purpose of this paper is to forecast the intraday values of stock indices using data mining techniques and compare the techniques’ performance in different markets to accomplish the best results.

This study investigates the intraday values (every 60th-minute closing value) of four different markets (namely, UK, Australia, India and China) spanning from April 1, 2017 to March 31, 2018. The forecasting performance of multivariate adaptive regression spline (MARSplines), support vector regression (SVR), backpropagation neural network (BPNN) and autoregression (1) are compared using statistical measures. Robustness evaluation is done to check the performance of the models on the relative ratios of the data.

MARSplines produces better results than the compared models in forecasting every 60th minute of selected stocks and stock indices. Next to MARSplines, SVR outperforms neural network and autoregression (1) models. The MARSplines proved to be more robust than the other models.

Forecasting provides a substantial benchmark for companies, which entails long-run operations. Significant profit can be earned by successfully predicting the stock’s future price. The traders have to outperform the market using techniques. Policy makers need to estimate the future prices/trends in the stock market to identify the link between the financial instruments and monetary policy which gives higher insights about the mechanism of existing policy and to know the role of financial assets in many channels. Thus, this study expects that the proposed model can create significant profits for traders by more precisely forecasting the stock market.

This study contributes to the high-frequency forecasting literature using MARSplines, SVR and BPNN. Finding the most effective way of forecasting the stock market is imperative for traders and portfolio managers for investment decisions. This study reveals the changing levels of trends in investing and expectation of significant gains in a short time through intraday trading.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

This study aims to provide a comprehensive review of hotel demand forecasting to identify its key fundamentals and evolution and future research directions and trends to advance the field.

Articles on hotel demand modeling and forecasting were identified and rigorously selected using transparent inclusion and exclusion criteria. A final sample of 85 empirical studies was obtained for comprehensive analysis through content analysis.

Synthesis of the literature highlights that hotel forecasting based on historical demand data dominates the research, and reservation/cancellation data and combined data gradually attracted research attention in recent years. In terms of model evolution, time series and AI-based models are the most popular models for hotel demand forecasting. Review results show that numerous studies focused on hybrid models and AI-based models.

To the best of the authors’ knowledge, this study is the first systematic review of the literature on hotel demand forecasting from the perspective of data source and methodological development and indicates future research directions.

本研究旨在对酒店需求预测进行全面回顾, 以确定其关键基础和演变以及未来的研究方向和趋势, 以推动该领域的发展。

使用严格和透明的纳入和排除的标准对酒店需求建模和预测的文章进行识别和选择。通过内容分析, 最终有 85个实证研究作为综合分析的样本。

综合文献发现, 基于历史需求数据的酒店预测在研究中占主导地位, 近年来预订/取消数据和组合数据逐渐引起研究关注。在模型演化方面, 时间序列和基于人工智能的模型是最受欢迎的酒店需求预测模型。审查结果表明, 许多研究都集中在混合模型和基于 AI 的模型上。

本研究是第一次从数据源和方法发展的角度对酒店需求预测文献进行系统回顾, 并指出未来的研究方向。

Este estudio tiene como objetivo proporcionar una revisión amplia de la previsión sobre la demanda hotelera a la hora de identificar sus fundamentos clave, la evolución y las direcciones y tendencias de investigación futuras para avanzar en el campo de estudio.

Se identificaron y seleccionaron de forma rigurosa artículos sobre modelado y previsión de la demanda hotelera utilizando criterios transparentes de inclusión y exclusión. Se obtuvo una muestra final de 85 estudios empíricos para su análisis integral a través del análisis de contenido.

La síntesis de la literatura destaca que la previsión hotelera basada en datos históricos de demanda ha dominado la investigación, y los datos de reserva/cancelación, así como los datos combinados han atraído gradualmente en los últimos años la atención de la investigación. En términos de evolución del modelo, las series temporales y los modelos basados en IA son los modelos más populares para la previsión de la demanda hotelera. Los resultados de la revisión muestran que numerosos estudios se han centrado en modelos híbridos y basados en IA.

Este estudio es la primera revisión sistemática de la literatura sobre la previsión de la demanda hotelera desde la perspectiva de la fuente de datos y el desarrollo metodológico e indica futuras líneas de investigación.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

The development of models for variables sampled at different frequencies has attracted substantial interest in the recent literature. In this article, we discuss classical and Bayesian methods of estimating mixed-frequency VARs, and use them for forecasting and structural analysis. We also compare mixed-frequency VARs with other approaches to handling mixed-frequency data.

This paper sets out to apply chaos theory to the prediction of stock returns using Greek and Turkish stock index data. The aim of the analysis is to empirically show whether the markets have informational efficiency, in a comparative perspective.

The research employs Grassberger and Procaccia's methodology in the time series analysis in order to estimate the correlation and minimum embedding dimensions of the corresponding strange attractor. To achieve out of the sample multistep ahead prediction, the paper gives the average for overall neighbours' projections of k‐steps into the future.

The results display the fact that the chaos theory is suitable to examine the time series of stock index returns. The empirical findings show that the stock markets are efficient in Greece, though in Turkey the market is predictable. The main practical implication of the findings is that the technical analysis works in Turkish markets and it is possible to beat the market, while in Greece the fundamental analysis works for equity trading.

The research results have both methodological and practical originality. On the theoretical side, the research shows how the chaos theory can be applied in financial time series analysis. The model is employed with data from Greece, as an EU member; and Turkey, as a candidate to the EU. The fact that the model works in Turkey implies that chaos theory can be used in emerging economies as a prediction model. On the practical side, the paper contributed to the previous literature by providing empirical evidence on market efficiency using a stochastic model.

The purpose of this study is to identify appropriate autoregressive conditional duration (ACD) models that can capture the dynamics of tick-by-tick mid-cap exchange traded funds (ETFs) for the period July 2017 to December 2017 and accurately predict future trade duration values. The forecasted durations are then used to demonstrate the practical usefulness of the ACD models in quantifying an intraday time-based risk measure.

Through six functional forms and six error distributions, 36 ACD models are estimated for eight mid-cap ETFs. The Akaike information criterion and Bayesian information criterion and the Ljung-Box test are used to evaluate goodness-of-fit while root mean square error and the Superior predictive ability test are applied to assess forecast accuracy.

The Box-Cox ACD (BACD), augmented Box-Cox ACD (ABACD) and additive and multiplicative ACD (AMACD) extensions are among the best fits. The results obtained prove that higher degrees of flexibility do not necessarily enhance goodness of fit and forecast accuracy does not always depend on model adequacy. BACD and AMACD models based on the generalised-F distribution generate the best forecasts, irrespective of the trading frequencies of the ETFs.

To the best of the authors’ knowledge, this is the first study that analyses the empirical performance of ACD models for high-frequency ETF data. Additionally, in comparison to previous works, a wider range of ACD models is considered on a reasonably longer sample period. The paper will be of interest to researchers in the area of market microstructure and to practitioners engaged in high-frequency trading.