Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Applications of Probabilistic Forecasting in Smart Grids : A Review

This paper reviews the recent studies and works dealing with probabilistic forecasting models and their applications in smart grids. According to these studies, this paper tries to introduce a roadmap towards decision-making under uncertainty in a smart grid environment. In this way, it firstly discusses the common methods employed to predict the distribution of variables. Then, it reviews how the recent literature used these forecasting methods and for which uncertain parameters they wanted to obtain distributions. Unlike the existing reviews, this paper assesses several uncertain parameters for which probabilistic forecasting models have been developed. In the next stage, this paper provides an overview related to scenario generation of uncertain parameters using their distributions and how these scenarios are adopted for optimal decision-making. In this regard, this paper discusses three types of optimization problems aiming to capture uncertainties and reviews the related papers. Finally, we propose some future applications of probabilistic forecasting based on the flexibility challenges of power systems in the near future.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Forecasting and Risk Management Techniques for Electricity Markets

This book focuses on the recent development of forecasting and risk management techniques for electricity markets. In addition, we discuss research on new trading platforms and environments using blockchain-based peer-to-peer (P2P) markets and computer agents. The book consists of two parts. The first part is entitled “Forecasting and Risk Management Techniques” and contains five chapters related to weather and electricity derivatives, and load and price forecasting for supporting electricity trading. The second part is entitled “Peer-to-Peer (P2P) Electricity Trading System and Strategy” and contains the following five chapters related to the feasibility and enhancement of P2P energy trading from various aspects

Predicting the Future

Due to the increased capabilities of microprocessors and the advent of graphics processing units (GPUs) in recent decades, the use of machine learning methodologies has become popular in many fields of science and technology. This fact, together with the availability of large amounts of information, has meant that machine learning and Big Data have an important presence in the field of Energy. This Special Issue entitled “Predicting the Future—Big Data and Machine Learning” is focused on applications of machine learning methodologies in the field of energy. Topics include but are not limited to the following: big data architectures of power supply systems, energy-saving and efficiency models, environmental effects of energy consumption, prediction of occupational health and safety outcomes in the energy industry, price forecast prediction of raw materials, and energy management of smart buildings

ISBIS 2016: Meeting on Statistics in Business and Industry

This Book includes the abstracts of the talks presented at the 2016 International Symposium on Business and Industrial Statistics, held at Barcelona, June 8-10, 2016, hosted at the Universitat Politècnica de Catalunya - Barcelona TECH, by the Department of Statistics and Operations Research. The location of the meeting was at ETSEIB Building (Escola Tecnica Superior d'Enginyeria Industrial) at Avda Diagonal 647. The meeting organizers celebrated the continued success of ISBIS and ENBIS society, and the meeting draw together the international community of statisticians, both academics and industry professionals, who share the goal of making statistics the foundation for decision making in business and related applications. The Scientific Program Committee was constituted by: David Banks, Duke University Amílcar Oliveira, DCeT - Universidade Aberta and CEAUL Teresa A. Oliveira, DCeT - Universidade Aberta and CEAUL Nalini Ravishankar, University of Connecticut Xavier Tort Martorell, Universitat Politécnica de Catalunya, Barcelona TECH Martina Vandebroek, KU Leuven Vincenzo Esposito Vinzi, ESSEC Business Schoo

Non Stationarity and Market Structure Dynamics in Financial Time Series

This thesis is an investigation of the time changing nature of financial markets. Financial markets are complex systems having an intrinsic structure defined by the interplay of several variables. The technological advancements of the ’digital age’ have exponentially increased the amount of data available to financial researchers and industry professionals over the last decade and, as a consequence, it has highlighted the key role of iterations amongst variables. A critical characteristic of the financial system, however, is its time changing nature: the multivariate structure of the systems changes and evolves through time. This feature is critically relevant for classical statistical assumptions and has proven challenging to be investigated and researched. This thesis is devoted to the investigation of this property, providing evidences on the time changing nature of the system, analysing the implications for traditional asset allocation practices and proposing a novel methodology to identify and predict ‘market states’. First, I analyse how classical model estimations are affected by time and what are the consequential effects on classical portfolio construction techniques. Focusing on elliptical models of daily returns, I present experiments on both in-sample and out-of-sample likelihood of individual observations and show that the system changes significantly through time. Larger estimation windows lead to stable likelihood in the long run, but at the cost of lower likelihood in the short-term. A key implication of these findings is that the optimality of fit in finance needs to be defined in terms of the holding period. In this context, I also show that sparse models and information filtering significantly cope with the effects of non stationarity avoiding the typical pitfalls of conventional portfolio optimization approaches. Having assessed and documented the time changing nature of the financial system, I propose a novel methodology to segment financial time series into market states that we call ICC - Inverse Covariance Clustering. The ICC methodology allows to study the evolution of the multivariate structure of the system by segmenting the time series based on their correlation structure. In the ICC framework, market states are identified by a reference sparse precision matrix and a vector of expectation values. In the estimation procedure, each multivariate observation is associated to a market state accordingly to a minimisation of a penalized distance measure (e.g. likelihood, mahalanobis distance). The procedure is made computationally very efficient and can be used with a large number of assets. Furthermore, the ICC methodology allows to control for temporal consistency,S making it of high practical relevance for trading systems. I present a set of experiments investigating the features of the discovered clusters and comparing it to standard clustering techniques. I show that the ICC methodology is successful at clustering different states of the markets in an unsupervised manner, outperforming baseline standard models. Further, I show that the procedure can be efficiently used to forecast off-sample future market states with significant prediction accuracy. Lastly, I test the significance of increasing number of states used to model equity returns and how this parameter relates to the number of observations and the time consistency of the states. I present experiments to investigate a) the likelihood of the overall model as more states are spanned, b) the relevance of additional regimes measured by the number of observations clustered. I found that the number of “market states” that optimally define the system is increasing with the time spanned and the number of observations considered