A Comparison of Machine Learning and Classical Demand Forecasting Methods: A Case Study of Ecuadorian Textile Industry

[EN] This document presents a comparison of demand forecasting methods, with the aim of improving demand forecasting and with it, the production planning system of Ecuadorian textile industry. These industries present problems in providing a reliable estimate of future demand due to recent changes in the Ecuadorian context. The impact on demand for textile products has been observed in variables such as sales prices and manufacturing costs, manufacturing gross domestic product and the unemployment rate. Being indicators that determine to a great extent, the quality and accuracy of the forecast, generating also, uncertainty scenarios. For this reason, the aim of this work is focused on the demand forecasting for textile products by comparing a set of classic methods such as ARIMA, STL Decomposition, Holt-Winters and machine learning, Artificial Neural Networks, Bayesian Networks, Random Forest, Support Vector Machine, taking into consideration all the above mentioned, as an essential input for the production planning and sales of the textile industries. And as a support, when developing strategies for demand management and medium-term decision making of this sector under study. Finally, the effectiveness of the methods is demonstrated by comparing them with different indicators that evaluate the forecast error, with the Multi-layer Neural Networks having the best results with the least error and the best performance.The authors are greatly grateful by the support given by the SDAS Research Group (https://sdas-group.com/).Lorente-Leyva, LL.; Alemany Díaz, MDM.; Peluffo-Ordóñez, DH.; Herrera-Granda, ID. (2021). 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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

Forecasting: theory and practice

peer reviewedForecasting 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

Influence of the scleral indentation technique on the re-detachment rate following retinal detachment surgery

Abstract Purpose The aim of this study was to determine whether the choice of scleral indentation technique during primary rhegmatogenous retinal detachment surgery has an influence on the risk of re-detachment. Methods We included retrospectively 154 eyes with a primary rhegmatogenous retinal detachment treated in the Eye Clinic Sulzbach/Saar Germany, who were operated on by two experienced surgeons using the same basic surgical setup. Surgeon A performed an external 360° indentation, shaved the vitreous base using the light pipe cap, and used the operating microscope (opm) for direct visualization. Surgeon B performed an external 360° indentation, shaved the vitreous base using a simple indentor, and used an endoillumination (light pipe) with the opm and a handheld widefield lens for direct visualization. Results Comparing both indentation procedures, 15.66% (13/83) of patients operated on by surgeon A and 9.86% (7/71) of patients operated on by surgeon B had a retinal re-detachment within a follow-up period of 6 months (adj. p = 0.64, two-proportion Z-test). Conclusion The rate of retinal re-detachment could be influenced by the indentation technique at the end of surgery favoring external indentation and internal visualization with an endoilluminator (chandelier light). We attribute this to the better visualization of the vitreous base facilitated by endoillumination. However, many variables play a role in the development of retinal re-detachment, requiring further studies with a larger number of patients. </jats:sec

Exploring the predictive power of news and neural machine learning models for economic forecasting

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A SAT Approach for Finding Sup-Transition-Minors

The cycle double cover conjecture is a famous longstanding unsolved conjecture in graph theory. It is related and can be reduced to the compatible circuit decomposition problem. Recently Fleischner et al. (2018) provided a sufficient condition for a compatible circuit decomposition, which is called SUD- K5-minor freeness. In a previous work we developed an abstract mathematical model for finding SUD-K5-minors and based on the model a mixed integer linear program (MIP). In this work we propose a respective boolean satisfiability (SAT) model and compare it with the MIP model in computational tests. Non-trivial symmetry breaking constraints are proposed, which improve the solving times of both models considerably. Compared to the MIP model the SAT approach performs significantly better. We use the faster algorithm to further test graphs of graph theoretic interest and were able to get new insights. Among other results we found snarks with 30 and 32 vertices that do not contain a perfect pseudomatching, that is a spanning subgraph consisting of K2 and K1;3 components, whose contraction leads to a SUD-K5-minor free graph