Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Investigating the applicability of Bayesian networks to demand forecasting during the final phase of support operations

A challenge faced by businesses that provide logistical support to systems is when the provision of those support services is no longer required. A typical example of such a situation is when military operations come to an end. In such cases, those companies that have a contract with the Armed Forces to provide maintenance support for the deployed systems, need to maintain those systems at minimum cost during that final phase, that is from the time the decision to stop the operations is announced until their very end. During the final phase, a challenging problem is forecasting the demand for spare parts, corresponding to equipment failures within the system. This is because the support context, the number of supported systems, the support equipment or even the operational demand can change during that period, and also because there can be very limited opportunities to place orders to cover demand. This thesis suggests that these types of problems can take advantage of the data that have been collected during the support operations prior to the initiation of the closing down process. Moreover, the thesis investigates the exploitation of these data by the use of Bayesian Networks to forecast the demand for spares that will be required for the provision of maintenance during the final phase. The research uses stochastically simulated Support Chain scenarios to generate data and also to evaluate different methods of constructing Bayesian Networks. The simulated scenarios differ in the demand context as well as in the complexity of the Equipment Breakdown Structure of the supported systems. The Bayesian Networks’ structure development methods that are tested include unsupervised machine learning, eliciting the structure from Subject Matter Experts, and two hybrid approaches that combine expert elicitation and machine learning. These models are compared to respective logistic regression models, as well as subject matter experts-adjusted single exponential smoothing forecasts. The comparison of the models is made using both accuracy metrics and accuracy implication metrics. These forecast models’ comparison methods are analysed in order to evaluate their appropriateness. The analyses have provided a number of novel outputs. The algebraic analysis of the accuracy metrics theoretically proves empirical problems that have been discussed in the literature but also reveals others. Regarding the accuracy implication metrics, the analysis shows that for the particular type of problems examined in this thesis –final phase problems – the accuracy implication metrics commonly applied are not enough to inform decision making, and a number of additional ones are required.The research shows that for the scenarios examined, the Bayesian Networks that had their structure learned using an unsupervised algorithm performed better in the accuracy metric than any of the other models. However, even though these Bayesian Networks also did well with the accuracy implication metrics, neither they, nor any of the others was consistently dominant. The reason for the discrepancy in the results between the accuracy and the accuracy implication metrics is that the latter are not only driven by how accurate the forecast model’s prediction is, but also by the model of the residual error and the bias

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases

Management: A continuing literature survey with indexes, March 1976

Management is a compilation of references to selected reports, journal articles, and other documents on the subject of management. This publication lists 368 documents originally announced in the 1975 issues of Scientific and Technical Aerospace Reports (STAR) or International Aerospace Abstracts (IAA). It includes references on the management of research and development, contracts, production, logistics, personnel, safety, reliability and quality control. It also includes references on: program, project and systems management; management policy, philosophy, tools, and techniques; decisionmaking processes for managers; technology assessment; management of urban problems; and information for managers on Federal resources, expenditures, financing, and budgeting

Towards a data-driven military: a multi-disciplinary perspective

Towards a data-driven military. A multi-disciplinary perspective assesses the use of data and information on modern conflict from different scientific and methodological disciplines, aiming to generate valuable contributions to the ongoing discourse on data, the military and modern warfare. Military Systems and Technology approaches the theme empirically by researching how data can enhance the utility of military materiel and subsequently accelerate the decision-making process. War Studies take a multidisciplinary approach to the evolution of warfare, while Military Management Studies take a holistic organisational and procedural approach. Based on their scientific protocols and research methods, the three domains put forward different research questions and perspectives, providing the unique character of this book

NASA thesaurus. Volume 1: Hierarchical Listing

There are over 17,000 postable terms and nearly 4,000 nonpostable terms approved for use in the NASA scientific and technical information system in the Hierarchical Listing of the NASA Thesaurus. The generic structure is presented for many terms. The broader term and narrower term relationships are shown in an indented fashion that illustrates the generic structure better than the more widely used BT and NT listings. Related terms are generously applied, thus enhancing the usefulness of the Hierarchical Listing. Greater access to the Hierarchical Listing may be achieved with the collateral use of Volume 2 - Access Vocabulary and Volume 3 - Definitions