1,456 research outputs found
Tourism demand modelling and forecasting : a review of recent research
2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe
Fuzzy Logic and Its Uses in Finance: A Systematic Review Exploring Its Potential to Deal with Banking Crises
The major success of fuzzy logic in the field of remote control opened the door to its application in many other fields, including finance. However, there has not been an updated and comprehensive literature review on the uses of fuzzy logic in the financial field. For that reason, this study attempts to critically examine fuzzy logic as an effective, useful method to be applied to financial research and, particularly, to the management of banking crises. The data sources were Web of Science and Scopus, followed by an assessment of the records according to pre-established criteria and an arrangement of the information in two main axes: financial markets and corporate finance. A major finding of this analysis is that fuzzy logic has not yet been used to address banking crises or as an alternative to ensure the resolvability of banks while minimizing the impact on the real economy. Therefore, we consider this article relevant for supervisory and regulatory bodies, as well as for banks and academic researchers, since it opens the door to several new research axes on banking crisis analyses using artificial intelligence techniques
Financial crises and bank failures: a review of prediction methods
In this article we provide a summary of empirical results obtained in several economics and operations research papers that attempt to explain, predict, or suggest remedies for financial crises or banking defaults, as well as outlines of the methodologies used. We analyze financial and economic circumstances associated with the US subprime mortgage crisis and the global financial turmoil that has led to severe crises in many countries. The intent of the article is to promote future empirical research that might help to prevent bank failures and financial crises.financial crises; banking failures; operations research; early warning methods; leading indicators; subprime markets
New Developments in Tourism and Hotel Demand Modeling and Forecasting
Abstract
Purpose
The purpose of the study is to review recent studies published from 2007-2015 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.
Design/Methodology/approach
Articles on tourism and hotel demand modeling and forecasting published in both science citation index (SCI) and social science citation index (SSCI) journals were identified and analyzed.
Findings
This review found 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, while 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.
Research limitations/implications
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.
Practical implications
This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.
Originality/value
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
A comprehensive review on the design and optimization of surface water quality monitoring networks
This is the final version. Available from Elsevier via the DOI in this record. The surface water quality monitoring network (WQMN) is crucial for effective water environment management. How to design an optimal monitoring network is an important scientific and engineering problem that presents a special challenge in the smart city era. This comprehensive review provides a timely and systematic overview and analysis on quantitative design approaches. Bibliometric analysis shows the chronological pattern, journal distribution, authorship, citation and country pattern. Administration types of water bodies and design methods are classified. The flexibility characteristics of four types of direct design methods and optimization objectives are systematically summarized, and conclusions are drawn from experiences with WQMN parameters, station locations, and sampling frequency and water quality indicators. This paper concludes by identifying four main future directions that should be pursued by the research community. This review sheds light on how to better design and construct WQMNs.Key-Area Research and Development Program of Guangdong ProvinceNational Natural Science Foundation of ChinaInnovation Project of Universities in Guangdong Province-Natural Scienc
Energy consumption forecasting: a proposed framework
With the development of underdeveloped countries and the digitization of societies,
energy consumption is expected to continue to show high growth in the coming
decades. While there is still a strong focus on fossil fuels for energy generation, the
implementation of energy policies is crucial to gradually shift to renewable sources
and the consequent reduction in CO2 emissions. Buildings are currently the sector that
consumes the most energy.
To contribute for a better energy consumption efficiency, it was proposed a
framework, to be applied to buildings or households, to allow users to know their
energy consumption and the possibility to forecast it.
Different data analysis techniques for time series were used to provide information
to the user about their energy consumption as well as to validate important data
characteristics, namely stationarity and the existence of seasonality, which can have
an impact in the forecasting models.
For the definition of the forecasting models, state of the art was done to identify
used models for energy consumption forecasting, and three models were tested for
both types of data, univariate and multivariate. For the univariate data, the tested
models were SARIMA, Holt-Winters and LSTM as for the multivariate data,
SARIMA with exogenous variables, Support Vector Regression and LSTM. After the
first execution of each model, hyperparameter tuning was done to conclude on the
improvement of the results and the robustness of the models for later application to the
framework.Com o desenvolvimento de países subdesenvolvidos e a digitalização das
sociedades, é esperado que o consumo de energia continue a apresentar um
crescimento elevado nas próximas décadas. Existindo ainda um grande foco em fontes
fósseis para a geração de energia, a implementação de políticas energéticas são cruciais
para a mudança gradual para energias renováveis e consequente redução de emissões
de CO2. Edifícios são atualmente o sector que mais energia consomem.
De forma a contribuir para uma melhor eficiência no consumo de energia foi
proposta uma framework, a aplicar em edifícios ou apartamentos, para possibilitar aos
utilizadores ter um conhecimento do seu consumo de energia bem como a previsão
desse mesmo consumo.
Diferentes técnicas de análise de dados para séries temporais foram utilizadas para
proporcionar informação ao utilizador sobre o seu consumo de energia bem como a
validação de caraterísticas importantes dos dados, nomeadamente a verificação da
estacionariedade e a existência da sazonalidade, que terão impacto no modelo de
previsão.
Para a definição dos modelos preditivos, foi feita uma revisão de literatura sobre
modelos utilizados atualmente para previsão do consumo de energia e testados três
modelos para os dois tipos de dados, univariados e multivariados. Para os dados
univariados os modelos testados foram SARIMA, Holt-Winters e LSTM e para os
dados multivariados SARIMA com variáveis exógenas, Support Vector Regression e
LSTM. Após a primeira execução de cada modelo, foi feita uma otimização dos
modelos para concluir na melhoria dos resultados previstos e na robustez dos modelos
para posterior aplicação na framework
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