Application of Grey Theory in the Construction of Impact Criteria and Prediction Model of Players’ Salary Structure

[[abstract]]Salaries of professional players are usually determined prior to the execution of the responsibilities assigned by the organizations and are often based on the expected future performance of these players as derived from their past achievement. The study first identifies criteria that would affect players’ salaries through literature reviews and then utilizes grey relational analysis (GRA) and grey prediction model to calculate weights of salary impact criteria, players’ annual performance index, and salary prediction for the coming year. The performance data of players from the Chinese Professional Baseball League (CPBL) are used in this study. The results are as follows: (i) CPBL teams do refer to players’ past performance records and future performance prediction when deciding on their salaries and (ii) future performance prediction must be made using at least a 3-year data set. The proposed prediction model is able to effectively provide relevant and useful information to the CPBL teams’ management during players’ salary adjustment.[[notice]]補正完

Application of Seasonal and Multivariable Grey Prediction Models for Short-Term Load Forecasting

Short-term electricity load forecasting is one of the most important operations in electricity markets. The success in the operations of electricity market participants partially depends on the accuracy of load forecasts. In this paper, three grey prediction models, which are seasonal grey model (SGM), multivariable grey model (GM (1,N)) and genetic algorithm based multivariable grey model (GAGM (1,N)), are proposed for short-term load forecasting problem in day-ahead market. The effectiveness of these models is illustrated with two real-world data sets. Numerical results show that the genetic algorithm based multivariable grey model (GAGM (1,N)) is the most efficient grey forecasting model through its better forecast accuracy

Retail forecasting: research and practice

This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice

Bass diffusion and grey models to forecast new tourism product: a case study of Tanah Aina Resort in Malaysia

Tourism demand forecasting has been acknowledged by researchers as tourism industry involves a large investment and gives a high return to the organisations and to the countries. Among various tourism demand researches that have been published, yet little attention that focus on the new tourism product forecasting. This study focuses on the application of Bass diffusion model (BDM) and grey Bass forecasting model to the new tourism product demand forecasting. Bass diffusion model is an influential model among researchers in forecasting the new product and grey forecasting model is popular because of its ability to handle as low as four data. The combination of these two models, called grey Bass forecasting model is used for the first time in the application of forecasting the new tourism product demand in Malaysia. The new tourism products studied are ecotourism resorts; Tanah Aina Fahad and Tanah Aina Farrah Soraya. Monthly data from Tanah Aina Fahad and Tanah Aina Farrah Soraya are collected from 2014 until 2018 and are converted to yearly data for the estimation of potential market. There are three parameters involved in both models namely; potential market, m, coefficient of innovation, pand coefficient of imitation,q. Parameters estimation method and different value of potential market are employed. The study finds that the grey Bass forecasting model has a better performance compared to the basic BDM for Tanah Aina Fahad dataset based on the evaluation of forecast using mean absolute percentage error. Besides, for Tanah Aina Farrah Soraya, BDM shows a better performance than grey Bass forecasting model but the value ofm gives a significant effect in the forecasting performance. Future research can be improved by using other methods in the estimation of parameters and applying the best values ofp andqto achieve the best forecast

Fit evaluation of virtual garment try-on by learning from digital pressure data

Presently, garment fit evaluation mainly focuses on real try-on, and rarely deals with virtual try-on. With the rapid development of E-commerce, there is a profound growth of garment purchases through the internet. In this context, fit evaluation of virtual garment try-on is vital in the clothing industry. In this paper, we propose a Naive Bayes-based model to evaluate garment fit. The inputs of the proposed model are digital clothing pressures of different body parts, generated from a 3D garment CAD software; while the output is the predicted result of garment fit (fit or unfit). To construct and train the proposed model, data on digital clothing pressures and garment real fit was collected for input and output learning data respectively. By learning from these data, our proposed model can predict garment fit rapidly and automatically without any real try-on; therefore, it can be applied to remote garment fit evaluation in the context of e-shopping. Finally, the effectiveness of our proposed method was validated using a set of test samples. Test results showed that digital clothing pressure is a better index than ease allowance to evaluate garment fit, and machine learning-based garment fit evaluation methods have higher prediction accuracies

Predicting product sales in fashion retailing: a data analytics approach

No mercado de retalho de moda, uma determinação errônea dos montantes a comprar de cada artigo pelos fornecedores, seja por excesso ou defeito, pode resultar em custos desnecessários de armazenamento ou vendas perdidas, respectivamente. Ambas as situações devem ser evitadas pelas empresas, como tal surge a necessidade de determinar as quantidades de compras de uma forma precisa. Atualmente, as empresas recolhem grandes quantidades de dados referentes às suas vendas e características dos seus produtos. No passado, essa informação raramente era analisada e integrada no processo de tomada de decisão. No entanto, o aumento da capacidade de processamento de informações promoveu o uso da análise de dados como meio para obter conhecimento e apoiar os responsáveis pela tomada de decisão com o objetivo de alcançar melhores resultados comerciais. Portanto, o desenvolvimento de modelos que utilizem os diferentes fatores que influenciam as vendas e produzem previsões precisas de vendas futuras representam uma estratégia muito promissora. Os resultados obtidos podem ser muito valiosos para as empresas, pois permitem que as empresas alinhem o valor a comprar aos fornecedores com as vendas potenciais.Este projeto visa explorar o uso de técnicas de extração de dados para otimizar as quantidades de compra de cada produto vendido por uma empresa de retalho de moda. O projeto resulta no desenvolvimento de um modelo que usa dados de vendas anteriores dos produtos com características semelhantes para prever a quantidade que a empresa venderá potencialmente dos novos produtos. O projeto usará como um caso de estudo uma empresa de retalho de moda portuguesa.Para validar o modelo, serão utilizadas várias medidas de regressão linear para quantificar a qualidade do modelo.In the retail context, an erroneous determination of the amounts to buy of each article from the suppliers, either by excess or defect, can result in unnecessary costs of storage or lost sales, respectively. Both situations should be avoided by companies, which promotes the need to determine purchase quantities efficiently. Currently companies collect huge amounts of data referring to their sales and products' features. In the past, that information was seldom analyzed and integrated in the decision making process. However, the increase of the information processing capacity has promoted the use of data analytics as a means to obtain knowledge and support decision makers inachieving better business outcomes. Therefore, the development of models which use the different factors which influences sales and produces precise predictions of future sales represents a very promising strategy. The results obtained could be very valuable to the companies, as they enable companies to align the amount to buy from the suppliers with the potential sales.This project aims at exploring the use of data mining techniques to optimize the amounts to buy of each product sold by a fashion retail company. The project results in the development of a model that uses past sales data of the products with similar characteristics to predict the quantity the company will potentially sell from the new products. The project will use as a case study a Portuguese fashion retail company.To validate the model it will be used several linear regression measures to quantify model quality

An Image Filter Based on Shearlet Transformation and Particle Swarm Optimization Algorithm

Digital image is always polluted by noise and made data postprocessing difficult. To remove noise and preserve detail of image as much as possible, this paper proposed image filter algorithm which combined the merits of Shearlet transformation and particle swarm optimization (PSO) algorithm. Firstly, we use classical Shearlet transform to decompose noised image into many subwavelets under multiscale and multiorientation. Secondly, we gave weighted factor to those subwavelets obtained. Then, using classical Shearlet inverse transform, we obtained a composite image which is composed of those weighted subwavelets. After that, we designed fast and rough evaluation method to evaluate noise level of the new image; by using this method as fitness, we adopted PSO to find the optimal weighted factor we added; after lots of iterations, by the optimal factors and Shearlet inverse transform, we got the best denoised image. Experimental results have shown that proposed algorithm eliminates noise effectively and yields good peak signal noise ratio (PSNR)

Improving Demand Forecasting: The Challenge of Forecasting Studies Comparability and a Novel Approach to Hierarchical Time Series Forecasting

Bedarfsprognosen sind in der Wirtschaft unerlässlich. Anhand des erwarteten Kundenbe-darfs bestimmen Firmen beispielsweise welche Produkte sie entwickeln, wie viele Fabri-ken sie bauen, wie viel Personal eingestellt wird oder wie viel Rohmaterial geordert wer-den muss. Fehleinschätzungen bei Bedarfsprognosen können schwerwiegende Auswir-kungen haben, zu Fehlentscheidungen führen, und im schlimmsten Fall den Bankrott einer Firma herbeiführen. Doch in vielen Fällen ist es komplex, den tatsächlichen Bedarf in der Zukunft zu antizipie-ren. Die Einflussfaktoren können vielfältig sein, beispielsweise makroökonomische Ent-wicklung, das Verhalten von Wettbewerbern oder technologische Entwicklungen. Selbst wenn alle Einflussfaktoren bekannt sind, sind die Zusammenhänge und Wechselwirkun-gen häufig nur schwer zu quantifizieren. Diese Dissertation trägt dazu bei, die Genauigkeit von Bedarfsprognosen zu verbessern. Im ersten Teil der Arbeit wird im Rahmen einer überfassenden Übersicht über das gesamte Spektrum der Anwendungsfelder von Bedarfsprognosen ein neuartiger Ansatz eingeführt, wie Studien zu Bedarfsprognosen systematisch verglichen werden können und am Bei-spiel von 116 aktuellen Studien angewandt. Die Vergleichbarkeit von Studien zu verbes-sern ist ein wesentlicher Beitrag zur aktuellen Forschung. Denn anders als bspw. in der Medizinforschung, gibt es für Bedarfsprognosen keine wesentlichen vergleichenden quan-titativen Meta-Studien. Der Grund dafür ist, dass empirische Studien für Bedarfsprognosen keine vereinheitlichte Beschreibung nutzen, um ihre Daten, Verfahren und Ergebnisse zu beschreiben. Wenn Studien hingegen durch systematische Beschreibung direkt miteinan-der verglichen werden können, ermöglicht das anderen Forschern besser zu analysieren, wie sich Variationen in Ansätzen auf die Prognosegüte auswirken – ohne die aufwändige Notwendigkeit, empirische Experimente erneut durchzuführen, die bereits in Studien beschrieben wurden. Diese Arbeit führt erstmals eine solche Systematik zur Beschreibung ein. Der weitere Teil dieser Arbeit behandelt Prognoseverfahren für intermittierende Zeitreihen, also Zeitreihen mit wesentlichem Anteil von Bedarfen gleich Null. Diese Art der Zeitreihen erfüllen die Anforderungen an Stetigkeit der meisten Prognoseverfahren nicht, weshalb gängige Verfahren häufig ungenügende Prognosegüte erreichen. Gleichwohl ist die Rele-vanz intermittierender Zeitreihen hoch – insbesondere Ersatzteile weisen dieses Bedarfs-muster typischerweise auf. Zunächst zeigt diese Arbeit in drei Studien auf, dass auch die getesteten Stand-der-Technik Machine Learning Ansätze bei einigen bekannten Datensät-zen keine generelle Verbesserung herbeiführen. Als wesentlichen Beitrag zur Forschung zeigt diese Arbeit im Weiteren ein neuartiges Verfahren auf: Der Similarity-based Time Series Forecasting (STSF) Ansatz nutzt ein Aggregation-Disaggregationsverfahren basie-rend auf einer selbst erzeugten Hierarchie statistischer Eigenschaften der Zeitreihen. In Zusammenhang mit dem STSF Ansatz können alle verfügbaren Prognosealgorithmen eingesetzt werden – durch die Aggregation wird die Stetigkeitsbedingung erfüllt. In Expe-rimenten an insgesamt sieben öffentlich bekannten Datensätzen und einem proprietären Datensatz zeigt die Arbeit auf, dass die Prognosegüte (gemessen anhand des Root Mean Square Error RMSE) statistisch signifikant um 1-5% im Schnitt gegenüber dem gleichen Verfahren ohne Einsatz von STSF verbessert werden kann. Somit führt das Verfahren eine wesentliche Verbesserung der Prognosegüte herbei. Zusammengefasst trägt diese Dissertation zum aktuellen Stand der Forschung durch die zuvor genannten Verfahren wesentlich bei. Das vorgeschlagene Verfahren zur Standardi-sierung empirischer Studien beschleunigt den Fortschritt der Forschung, da sie verglei-chende Studien ermöglicht. Und mit dem STSF Verfahren steht ein Ansatz bereit, der zuverlässig die Prognosegüte verbessert, und dabei flexibel mit verschiedenen Arten von Prognosealgorithmen einsetzbar ist. Nach dem Erkenntnisstand der umfassenden Literatur-recherche sind keine vergleichbaren Ansätze bislang beschrieben worden