Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching

Personalization in marketing aims at improving the shopping experience of customers by tailoring services to individuals. In order to achieve this, businesses must be able to make personalized predictions regarding the next purchase. That is, one must forecast the exact list of items that will comprise the next purchase, i.e., the so-called market basket. Despite its relevance to firm operations, this problem has received surprisingly little attention in prior research, largely due to its inherent complexity. In fact, state-of-the-art approaches are limited to intuitive decision rules for pattern extraction. However, the simplicity of the pre-coded rules impedes performance, since decision rules operate in an autoregressive fashion: the rules can only make inferences from past purchases of a single customer without taking into account the knowledge transfer that takes place between customers. In contrast, our research overcomes the limitations of pre-set rules by contributing a novel predictor of market baskets from sequential purchase histories: our predictions are based on similarity matching in order to identify similar purchase habits among the complete shopping histories of all customers. Our contributions are as follows: (1) We propose similarity matching based on subsequential dynamic time warping (SDTW) as a novel predictor of market baskets. Thereby, we can effectively identify cross-customer patterns. (2) We leverage the Wasserstein distance for measuring the similarity among embedded purchase histories. (3) We develop a fast approximation algorithm for computing a lower bound of the Wasserstein distance in our setting. An extensive series of computational experiments demonstrates the effectiveness of our approach. The accuracy of identifying the exact market baskets based on state-of-the-art decision rules from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019

A Unified Statistical Framework for Evaluating Predictive Methods

Predictive analytics is an important part of the business intelligence and decision support systems literature and likely to grow in importance with the emergence of big data as a discipline. Despite their importance, the accuracy of predictive methods is often not assessed using statistical hypothesis tests. Furthermore, there is no commonly agreed upon standard as to which questions should be examined when evaluating predictive methods. We fill this gap by defining three questions that involve the overall and comparative predictive accuracy of the new method. We then present a unified statistical framework for evaluating predictive methods that can be used to address all three of these questions. The framework is particularly versatile and can be applied to most problems and datasets. In addition to these practical advantages over hypotheses tests used in previous literature, the framework has the theoretical advantage that it is not necessary to assume a normal distribution

Projeções de receitas líquidas de empresas de capital aberto do subsetor de comércio com diferentes metodologias preditivas em cenário de pandemia

TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Engenharia de ProduçãoO planejamento das vendas no subsetor de comércio no Brasil tem sua complexidade associada às peculiaridades que o caracterizam tais como: variações dos canais de vendas utilizados pelos consumidores, forte sazonalidade, entrada de competidores nacionais e internacionais entre outros. Nesse contexto, a previsão de vendas possui uma enorme importância no planejamento de uma organização e conseguir a maior acuracidade nesta atividade revela-se como um valoroso recurso estratégico capaz contribuir para a necessária manutenção de sua competitividade. Com o passar dos anos houve um grande avanço na tecnologia de coleta e tratamento de dados e em conjunto diversos modelos estatísticos preditivos foram ganhando destaque para serem aplicados em cenários cada vez mais complexos. Métodos de previsões são usados para identificar padrões em séries temporais e tem a capacidade de prever períodos futuros com acuracidade cada vez maior. No final do ano de 2019, a economia mundial foi impactada com a pandemia da COVID-19 e o subsetor de comércio foi amplamente impactado pelo fechamento de diversos pontos comerciais e uma forte migração dos consumidores para o e-commerce. Todo esse impacto trouxe uma complexidade ainda maior aos gestores para conseguirem prever demandas futuras e tomarem decisões estratégicas. O presente trabalho se propõe em aplicar diferentes metodologias preditivas nas principais empresas de capital aberto do subsetor de comércio usando as séries temporais da receita operacional líquida levando em consideração o impacto da pandemia. A conclusão desse estudo quantitativo se faz com a comparação de acuracidade entre os modelos.Sales planning in retail business in Brazil has its complexity associated with the peculiarities that characterize it such as: variations in the sales channels used by consumers, strong seasonality, entry of national and international competitors, among others. In this context, sales forecast has an enormous importance in the planning of an organization and achieving the greatest accuracy in this activity reveals itself as a valuable strategic resource capable of contributing to the necessary maintenance of its needs. Over the years, there has been a great advance in data collection and processing technology and, together, several predictive statistical models have been gaining prominence to be applied in increasingly complex scenarios. Forecasting methods are used to identify patterns in time series and have the ability to predict future values with increasing accuracy. At the end of 2019, the world economy was impacted by the COVID-19 pandemic and the retail subsector was largely impacted by the closure of several commercial points and a strong migration of consumers to e-commerce. All this impact brought even greater complexity to managers to be able to predict future demands and make strategic decisions. This work proposes to apply different predictive methodologies in the biggest retail publicly traded companies in retail subsector using the time series of net operating revenue given how the impact of the pandemic. The conclusion of this quantitative study is made with an accuracy comparison between the models

A New Business Mode for FTs Chain in an E-Commerce Environment

With the rise in the online demand for fashion and textiles (FTs) along with the development of e-commerce, a business mode called drop-shipping mode has emerged. Despite the fact that the drop-shipping mode has many merits, this method has less earning power compared with the traditional business mode. This study proposes a mix business mode for FTs chains in an e-commerce environment. Traditional and drop-shipping modes are special cases of the mix mode. In addition, a generalized model is built to analyze the profitability of FTs chains. Our study shows that, in most cases, the mix mode improves overall profit of FTs chain. Moreover, we consider the seasonality and the short life cycle of fashion items in analyzing the relationship between the e-retailer's optimal inventory level and demand distribution parameters. The numerical example shows that, by changing their inventory level, e-retailers can address the demand fluctuation using the mix mode. The proposed mix mode employs both business modes to enhance the profitability of a FTs chain. As such, the mix mode is an effective method to address demand fluctuation for FTs in an e-commerce environment

Forecasting Irregular Seasonal Power Consumption. An Application to a Hot-Dip Galvanizing Process

[EN] The method described in this document makes it possible to use the techniques usually applied to load prediction efficiently in those situations in which the series clearly presents seasonality but does not maintain a regular pattern. Distribution companies use time series to predict electricity consumption. Forecasting techniques based on statistical models or artificial intelligence are used. Reliable forecasts are required for efficient grid management in terms of both supply and capacity. One common underlying feature of most demand-related time series is a strong seasonality component. However, in some cases, the electricity demanded by a process presents an irregular seasonal component, which prevents any type of forecast. In this article, we evaluated forecasting methods based on the use of multiple seasonal models: ARIMA, Holt-Winters models with discrete interval moving seasonality, and neural networks. The models are explained and applied to a real situation, for a node that feeds a galvanizing factory. The zinc hot-dip galvanizing process is widely used in the automotive sector for the protection of steel against corrosion. It requires enormous energy consumption, and this has a direct impact on companies' income statements. In addition, it significantly affects energy distribution companies, as these companies must provide for instant consumption in their supply lines to ensure sufficient energy is distributed both for the process and for all the other consumers. The results show a substantial increase in the accuracy of predictions, which contributes to a better management of the electrical distribution.Trull, O.; García-Díaz, JC.; Peiró Signes, A. (2021). Forecasting Irregular Seasonal Power Consumption. An Application to a Hot-Dip Galvanizing Process. Applied Sciences. 11(1):1-24. https://doi.org/10.3390/app11010075S12411

Modelo para la predicción y evaluación de la demanda de agua potable mediante redes neuronales artificiales de la Empresa Pública de Agua Potable y Alcantarillado Antonio Ante

Diseñar un modelo para el pronóstico de la demanda de agua potable de la empresa EPAA-AA mediante redes neuronales artificiales que a futuro garantice la distribución eficiente del recurso hídrico.El presente trabajo de grado tiene como objetivo principal pronosticar la demanda de agua potable de los años 2023 y 2024 con los datos de la Empresa Pública de Agua Potable y Alcantarillado de Antonio Ante. Se ha utilizado redes neuronales artificiales en busca de mejorar la distribución del recurso hídrico dentro del cantón. La investigación documental relacionada con redes neuronales artificiales y su funcionamiento permitió establecer las bases teóricas para un correcto desarrollo de la investigación. Con el análisis de la estacionalidad y autocorrelación, se pudo identificar el comportamiento y la tendencia que adopta la demanda de agua potable. Sin dejar de lado los usuarios que emplean a diario este recurso. Con la comparación de modelos de pronósticos de Regresión Lineal y SARIMA (2,0,2) (1,0,1), se pudo probar el comportamiento y el error de cada uno. La métrica de comparación fue el RMSE con un 148,12 para RNA, en el SARIMA arrojó un resultado de 12459,1 y el de Regresión Lineal un valor de 22560,39. Dejando en evidencia que el mejor modelo para la demanda de agua potable es el de Redes Neuronales Artificiales. Con los resultados obtenidos de la demanda de agua potable para los dos siguientes años, la empresa logrará cubrir este requerimiento, sin embargo, esta debe centrarse en reducir el índice de agua no contabilizada y aumentar el porcentaje de continuidad del servicio para evitar desabastecimiento y brindar un servicio de calidad.Ingenierí

Previsão de vendas na presença de um elevado número de variáveis : um estudo de caso de itens intra e inter-categoria

A presente pesquisa teve como objetivo verificar se séries históricas referentes a Intra e Inter-categorias são capazes de melhorar o modelo de previsão de vendas de curto prazo para o varejo. Trata-se de um estudo de caso utilizando regressões múltiplas e o método de seleção de variáveis LASSO (Least Absolute Shrinkage And Selection Operator). Os objetivos específicos consistiram em: (1) confirmar empiricamente a existência de itens complementares e substitutos em Intra e Intercategorias; (2) propor um modelo de previsão que leve em consideração séries de Intra e Inter-categorias; (3) comparar os resultados encontrados entre o modelo de previsão com somente uma série histórica e o modelo proposto com Intra e Intercategorias; (4) identificar se existe diferença entre os resultados do modelo com séries Intra e Inter-categorias. Assim, os principais resultados identificados revelam que foi comprovada a existência de itens complementares e substitutos em Intra e Inter-categorias no nível de gramatura. Além disso, os resultados demonstraram maior prevalência de itens complementares, o que representa em média 88,8% das interações, sendo os demais 11,2% substitutos; os resultados apontam que 83,8% da melhoria dos resultados do RMSE são provenientes das séries Intra-categoria, o que representa a maioria expressiva da contribuição. Dentro deste percentual, a redução média do RMSE foi de 56,30%. Entretanto, o estudo destaca que séries Inter-categorias também são capazes de contribuir com 16,2% para melhorar a acurácia, demonstrando assim uma redução do erro e comprovando a existência de interação entre séries ao longo das categorias. Por fim, conclui-se que a utilização de séries pertencentes apenas a Intra-categoria para compor o modelo de previsão consegue melhorar a acurácia na maioria dos casos, e que a redução alcançada atinge resultados satisfatórios.FAPE

Diseño de la red de la cadena de suministro de una marca de coches eléctricos para su entrada en Europa

[ES] El objetivo de este trabajo es conectar herramientas de optimización con un caso práctico de diseño de red de cadena de suministro. Para ello, se considera el estudio de un sector como el de la movilidad eléctrica, que cuenta con grandes expectativas de crecimiento. Se pretende modelar una situación real supuesta por un cliente para planificar y programar su producción en los próximos años, a fin de cumplir con la estrategia marcada internamente. En este trabajo se resuelve una decisión de negocio fundamental para un fabricante de vehículos eléctricos, que busca optimizar el diseño de su red en la cadena de suministro. Para ello, se emplean la optimización matemática mediante un modelado del caso de estudio, previa búsqueda y análisis de información del mercado empleando también cierta información proporcionada por el cliente. En primer lugar, tras una minería de datos de matriculaciones en los paises de estudio, se desarrolla una estimación de la demanda de coches eléctricos. Esta estimación es necesaria para, junto con la información y objetivos proporcionados por el cliente, realizar el modelado del caso y su posterior resolución. El caso de estudio supone el modelado de un problema de programación lineal, que resuelve ante un conjunto de fábricas y regiones de demanda la mejor opción de configuración para el cliente, a fin de conocer el mínimo coste posible con el que suministrar la demanda esperada para los próximos cuatro años. Para ello, se consideran las diferentes fábricas de las que dispone o tiene al alcance comprar el cliente, así como los costes fijos y variables asociados. Mediante el lenguaje de programación Python, y haciendo uso del paquete de optimización lineal PuLP de código abierto y el de estadística Statsmodels, se resuelve el modelo que, una vez descrito en términos matemáticos, es implementado en este lenguaje. De esta forma, se emplea la computación para resolver el problema, obteniendo una solución óptima para el cliente que le permita estimar el coste mínimo total de la red para los cuatro próximos años. Se proporcionan los resultados de qué fábricas comprar o emplear en caso de que estén disponibles, así como qué cantidades se envían desde cada fábrica a cada región de demanda, en cada año analizado[EN] The objective of this work is to connect optimization tools with a supply chain network design case study. For this, the study of a sector such as electric mobility is considered, a sector which has great growth expectations. The aim is to model a real situation assumed by a client to plan and schedule its production in the coming years, in order to comply with the strategy set internally. In this work a fundamental business decision for an electric vehicle manufacturer, who seeks to optimize the design of its network in the Supply Chain is solved. Mathematical optimization is used through a modeling of the case study, after searching and analyzing market information, also using information provided by the client. In the first place, after a data mining of registrations in the studied countries, an estimation of the demand for electric cars is developed. This estimate is necessary, together with the informationand objectives provided by the client, to carry out the modeling of the case and its subsequent resolution. The case study involves the modeling of a linear programming problem, which solves the best configuration option for the customer before a set of factories and demand regions, in order to know the minimum possible cost with which to supply the expected demand for the next four years. Thus, the different factories that the customer has or is able to buy are considered, as well as the associated fixed and variable costs.Using the Python programming language, and making use of the open source PuLP linear optimization package and the Statsmodels statistics package, the model is solved which, once described in mathematical terms, is implemented in this language. This way, computing is used to solve the problem, obtaining an optimal solution for the client that allows them to estimate the minimum total cost of the suplyfor the next four years. The results of which factories to buy or employ if available, are provided, as well as which quantities are shipped from each factory to each demand region, in each year analyzed.Fullana Fuster, P. (2020). Diseño de la red de la cadena de suministro de una marca de coches eléctricos para su entrada en Europa. Universitat Politècnica de València. http://hdl.handle.net/10251/159776TFG

Advanced Methods of Power Load Forecasting

This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load