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This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.

Covid-19 pandemic is a unique and extraordinary situation for the globe, which has potentially disrupted almost all aspects of life. In this global crisis, the tourism and hospitality sector has collapsed in almost all parts of the world, and the same is true for India. Therefore, this paper aims to investigate the impact of Covid-19 on the Indian tourism industry.

This study develops an appropriate model to forecast the expected loss of foreign tourist arrivals (FTAs) in India for 10 months. Since the FTAs follow a seasonal trend, seasonal autoregressive integrated moving average (SARIMA) method has been employed to forecast the expected FTAs in India from March 2020 to December 2020. The results of the proposed model are then compared with the ones obtained by Holt-Winter's (H-W) model to check the robustness of the proposed model.

The SARIMA model seeks to manifest the monthly arrival of foreign tourists and also elaborates on the progressing expected loss of foreign tourists arrive for the next three quarters is approximately 2 million, 2.3 million and 3.2 million, respectively. Thus, in the next three quarters, there will be an enormous downfall of FTAs, and there is a need to adopt appropriate measures. The comparison demonstrates that SARIMA is a better model than H-W model.

Several studies have been reported on pandemic-affected tourism sectors using different techniques. The earlier pandemic outbreak was controlled and region-specific, but the Covid-19 eruption is a global threat having potential ramifications and strong spreading power. This work is one of the first attempts to study and analyse the impact of Covid-19 on FTAs in India.

The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution.

The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments.

Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes.

The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept.

The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

Business models are increasingly recognized as a concept to support innovation in organizations. The implementation and operation of a new or altered business model involves the (re-)design of an organization's business processes and their successful execution. This study reviews and synthesizes the existing body of literature to guide organizations in systematically moving from a business model design to the implementation and operation of the business model through their underlying business processes.

A systematic literature review of the methods that bridge business models and business processes is performed. The selected 34 studies are classified according to the method's characteristics and the support in the design, implementation and operation of business models.

The results of the systematic review provide an overview of existing methods that organizations can adopt when moving from business model design into the implementation and operation of their business model using processes.

This work provides a comprehensive overview and detailed insight into the existing methods that align business models and business processes. It increases the understanding on how these two concepts can be synthesized to support more effective digital innovation in organizations. Based on the review results, knowledge gaps are identified and an agenda for future research bridging the fields of business models and business processes is proposed.

This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress.

A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models.

The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models.

The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.

In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting (STLF). A HTM Spatial Pooler (HTM-SP) stage is used to continually form sparse distributed representations (SDRs) from a univariate load time series data, a temporal aggregator is used to transform the SDRs into a sequential bivariate representation space and an overlap classifier makes temporal classifications from the bivariate SDRs through time. The comparative performance of HTM on several daily electrical load time series data including the Eunite competition dataset and the Polish power system dataset from 2002 to 2004 are presented. The robustness performance of HTM is also further validated using hourly load data from three more recent electricity markets. The results obtained from experimenting with the Eunite and Polish dataset indicated that HTM will perform better than the existing techniques reported in the literature. In general, the robustness test also shows that the error distribution performance of the proposed HTM technique is positively skewed for most of the years considered and with kurtosis values mostly lower than a base value of 3 indicating a reasonable level of outlier rejections.

This study aims to evaluate the impact of climate change on some specific areas of agricultural production in Quang Nam Province, including assessing the possibility of losing agricultural land owing to sea level rise; assessing the impact on rice productivity; and, assessing the impact on crop water demand.

This study used the method of collecting and processing statistics data; method of analysis, comparison and evaluation; method of geographic information system; method of using mathematical model; and method of professional solution, to assess the impacts of climate change.

Evaluation results in Quang Nam Province show that, by the end of the 21st century, winter–spring rice productivity may decrease by 33%, while summer–autumn rice productivity may decrease by 49%. Under representative concentration pathway (RCP) 4.5 scenario, water demand increases by 31.1% compared to the baseline period, of which the winter–spring crop increases by 28.4%, and the summer–autumn crop increased by 34.3%. Under RCP 8.5 scenario, water demand increases by 54.1% compared to the baseline period, of which the winter–spring crop increases by 46.7%, and the summer–autumn crop increased by 63.1%. The area of agricultural land likely to be inundated by sea level rise at 50 cm is 418.32 ha, and at 80 cm, it is 637.07 ha.

To propose adaptation solution to avoid the impacts of climate change on agriculture, it is necessary to consider about the impact on losing land for agriculture, the impact on rice productivity, assess the impact on crop water demand and other. The result of this assessment is useful for policymakers for forming the agriculture development plan.

Accurate values for infiltration rate are important to reliably estimate heat losses from buildings. Infiltration rate is rarely measured directly, and instead is usually estimated using algorithms or data from fan pressurisation tests. However, there is growing evidence that the commonly used methods for estimating infiltration rate are inaccurate in UK dwellings. Furthermore, most prior research was conducted during the winter season or relies on single measurements in each dwelling. Infiltration rates also affect the likelihood and severity of summertime overheating. The purpose of this work is to measure infiltration rates in summer, to compare this to different infiltration estimation methods, and to quantify the differences.

Fifteen whole house tracer gas tests were undertaken in the same test house during spring and summer to measure the whole building infiltration rate. Eleven infiltration estimation methods were used to predict infiltration rate, and these were compared to the measured values. Most, but not all, infiltration estimation methods relied on data from fan pressurisation (blower door) tests. A further four tracer gas tests were also done with trickle vents open to allow for comment on indoor air quality, but not compared to infiltration estimation methods.

The eleven estimation methods predicted infiltration rates between 64 and 208% higher than measured. The ASHRAE Enhanced derived infiltration rate (0.41 ach) was closest to the measured value of 0.25 ach, but still significantly different. The infiltration rate predicted by the “divide-by-20” rule of thumb, which is commonly used in the UK, was second furthest from the measured value at 0.73 ach. Indoor air quality is likely to be unsatisfactory in summer when windows are closed, even if trickle vents are open.

The findings have implications for those using dynamic thermal modelling to predict summertime overheating who, in the absence of a directly measured value for infiltration rate (i.e. by tracer gas), currently commonly use infiltration estimation methods such as the “divide-by-20” rule. Therefore, infiltration may be overestimated resulting in overheating risk and indoor air quality being incorrectly predicted.

Direct measurement of air infiltration rate is rare, especially multiple tests in a single home. Past measurements have invariably focused on the winter heating season. This work is original in that the tracer gas technique used to measure infiltration rate many times in a single dwelling during the summer. This work is also original in that it quantifies both the infiltration rate and its variability, and compares these to values produced by eleven infiltration estimation methods.