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Padel is one of the fastest growing sports in Sweden. Its popularity rose significantly during the Covid-19 pandemic in 2020, as many other types of sport facilities closed, and people had more flexible work schedules due to remote work. This paper is an analysis on the monthly occupancy of indoor padel facilities in Sweden between January 2018 and April 2022. It aims to answer to what degree a machine learning algorithm can predict the occupancy for a given padel facility and which key features have the largest impact on the occupancy. With these findings, it is possible to estimate the revenue for a given padel facility and therefore be used to identify which type of padel facilities have the biggest opportunity to succeed from an economical perspective. This article reviews the literature regarding different methods of machine learning, in this case, applied to booking systems and occupancy estimations. The reviewed literature also presents the most common evaluation metrics used for comparing different machine learning models. This study analyses the relationship between the occupancy level of a given padel facility and 12 input features, related to the padel facility in question, with a random forest regression model. This work results in a model that achieved a R2 score of 49% and a mean absolute error of 11%. The input features ranked according to the largest impact on the model’s estimation are (with the mean of all absolute SHAP values written in parentheses): Year (7.71), Month (5.23), Average Income in municipality (4.13), Driving Time from municipality Centre (2.35), Population of municipality (1.97), Padel Slots in municipality (1.27), Padel Slots in facility (1.27), Average Court Price (1.12), Tennis Slots in municipality (0.73), Badminton Slots in municipality (0.55), Squash Slots in municipality (0.44) and Golf Slots in municipality (0.26). Padel facilities had the highest average occupancy in 2020. The Covid-19 pandemic is likely a significant contributor to this, due to the shutdown of offices and many types of training venues. Therefore, Year has the largest impact on the model’s estimation. Occupancy of indoor facilities follows a seasonal trend, where it tends to be highest in December and January and lowest in June and July. This trend can partly be explained by a larger demand for indoor sport activities during winter and increased competition from outside padel facilities and other activities during summer. Because of this, Month had the second largest impact on the model’s estimation. / Padel är en av de snabbast växande sporterna i Sverige. Dess popularitet ökade avsevärt under Covid-19-pandemin i 2020, främst på grund av att många andra typer av sportanläggningar stängdes ner och människor hade mer flexibla arbetsscheman på grund av distansarbete. Den här uppsatsen är en analys av den månatliga beläggningen av inomhuspadelanläggningar i Sverige mellan januari 2018 och april 2022. Studien syftar till att svara på i vilken grad en maskininlärningsalgoritm kan förutsäga beläggningen för en given padelanläggning och vilka nyckelfunktioner som har störst inverkan på beläggningen. Med dessa insikter är det möjligt att uppskatta intäkterna för en given padelanläggning och kan därför användas vilka typer av padelanläggningar som har störst möjlighet att vara framgångsrika ur ett ekonomiskt perspektiv. Den granskade litteraturen studerar olika maskininlärningsmetoder tillämpad i områden som bokningssystemsanalys och beläggningsgradsstudier, samt presenterar de vanligaste utvärderingsmåtten som används för att jämföra metoderna. Denna studie analyserar sambandet mellan beläggningsgraden för en given padelanläggning och 12 inputparametrar, relaterade till padelanläggningen i fråga med hjälp av en random forest regressionsalgoritm. Detta arbete resulterar i en modell som uppnådde ett R2 värde på 49% och en genomsnittlig absolut avvikelse på 11 %. Inputparametrarna rangordnade enligt den största påverkan på modellens uppskattning är (med medelvärdet av alla absoluta SHAP-värden skrivna inom parentes): År (7.71), Månad (5.23), Genomsnittlig Inkomst i kommunen (4.13), Körtid mellan anläggning och kommunens centrum (2.35), Kommunens befolkningsmängd (1.97), Antal padeltider i kommunen (1.27), Padeltider i anläggningen(1.27), Genomsnittlig pris för bana(1.12), Tennistider i kommunen (0.73), Badmintontider i kommunen (0.55), Squashtider i kommunen (0.44) och Golftider i kommunen (0.26). Padelanläggningar hade högsta genomsnittliga beläggningsgraden under 2020. Covid-19-pandemin är sannolikt en betydande bidragande orsak till detta på grund av nedläggningen av kontor och andra sportanläggningar. Därför har inputparametern År den största inverkan på modellens uppskattning. Beläggningen av inomhusanläggningar följer en säsongsmässig trend, där den tenderar att vara högst i januari och lägst i juli. Denna trend kan delvis förklaras av en större efterfrågan på inomhussportaktiviteter under vintern och ökad konkurrens från utomstående padelanläggningar och andra aktiviteter under sommaren. På grund av detta hade Månad den näst största påverkan på modellens uppskattning.

Surprise is an important component of serendipity. In this research, we address the problem of measuring the capacity of a recommender system at embedding surprise in its recommendations. We show that changes in surprise of an item owing to the growth in user experience, as well as to the increase in the number of items in the repository, are not taken into account by the current metrics and evaluation methods. As a result, in so far as the time elapsed between two measurements grows, they become increasingly incommensurable. This poses as an additional challenge in the assessment of the degree to which a recommender is exposed to unfavourable conditions, such as over-specialisation or filter bubble. We argue that a) surprise is a finite resource in any recommender system, b) there are limits to the amount of surprise that can be embedded in a recommendation, and c) these limits allow us to create a scale up in which two measurements that were taken at different moments can be directly compared. By adopting these ideas as premises, we applied the deductive method to define the concepts of maximum and minimum potential surprises and designed a surprise metric called \"normalised surprise\" that employs these limits. Our main contribution is an evaluation method that estimates the normalised surprise of a system. Four experiments were conducted to test the proposed metrics. The aim of the first and the second experiments was to validate the quality of the estimates of minimum and maximum potential surprise values obtained by means of a greedy algorithm. The first experiment employed a synthetic dataset to explore the limits to surprise to a user, and the second one employed the Movielens-1M to explore the limits to surprise that can be embedded in a recommendation list. The third experiment also employed the Movielens-1M dataset and was designed to investigate the effect that changes in item representation and item comparison exert on surprise. Finally, the fourth experiment compares the proposed and the current state-of-the-art evaluation method in terms of their results and execution times. The results obtained from the experiments a) confirm that the quality of the estimates of potential surprise are adequate for the purpose of evaluating normalised surprise; b) show that the item representation and comparison model that is adopted has a strong effect on surprise; and c) indicate an association between high degrees of surprise and negatively skewed pairwise distance distributions, and also indicate a significant difference in the average normalised surprise of recommendations produced by a factorisation algorithm when the surprise employs the cosine or the Euclidean distance / A surpresa é um componente importante da serendipidade. Nesta pesquisa, abordamos o problema de medir a capacidade de um sistema de recomendação de incorporar surpresa em suas recomendações. Mostramos que as mudanças na surpresa de um item, devidas ao crescimento da experiência do usuário e ao aumento do número de itens no repositório, não são consideradas pelas métricas e métodos de avaliação atuais. Como resultado, na medida em que aumenta o tempo decorrido entre duas medições, essas se tornam cada vez mais incomensuráveis. Isso se apresenta como um desafio adicional na avaliação do grau em que um sistema de recomendação está exposto a condições desfavoráveis como superespecialização ou filtro invisível. Argumentamos que a) surpresa é um recurso finito em qualquer sistema de recomendação; b) há limites para a quantidade de surpresa que pode ser incorporada em uma recomendação; e c) esses limites nos permitem criar uma escala na qual duas medições que foram tomadas em momentos diferentes podem ser comparadas diretamente. Ao adotar essas ideias como premissas, aplicamos o método dedutivo para definir os conceitos de surpresa potencial máxima e mínima e projetar uma métrica denominada \"surpresa normalizada\", que emprega esses limites. Nossa principal contribuição é um método de avaliação que estima a surpresa normalizada de um sistema. Quatro experimentos foram realizados para testar as métricas propostas. O objetivo do primeiro e do segundo experimentos foi validar a qualidade das estimativas de surpresa potencial mínima e máxima obtidas por meio de um algoritmo guloso. O primeiro experimento empregou um conjunto de dados sintético para explorar os limites de surpresa para um usuário, e o segundo empregou o Movielens-1M para explorar os limites da surpresa que pode ser incorporada em uma lista de recomendações. O terceiro experimento também empregou o conjunto de dados Movielens-1M e foi desenvolvido para investigar o efeito que mudanças na representação de itens e na comparação de itens exercem sobre a surpresa. Finalmente, o quarto experimento compara os métodos de avaliação atual e proposto em termos de seus resultados e tempos de execução. Os resultados que foram obtidos dos experimentos a) confirmam que a qualidade das estimativas de surpresa potencial são adequadas para o propósito de avaliar surpresa normalizada; b) mostram que o modelo de representação e comparação de itens adotado exerce um forte efeito sobre a surpresa; e c) apontam uma associação entre graus de surpresa elevados e distribuições assimétricas negativas de distâncias, e também apontam diferenças significativas na surpresa normalizada média de recomendações produzidas por um algoritmo de fatoração quando a surpresa emprega a distância do cosseno ou a distância Euclidiana

Made available in DSpace on 2016-04-25T16:44:44Z (GMT). No. of bitstreams: 1 Mario Jose Montini.pdf: 1228889 bytes, checksum: 8ecf9e824f548c285293119b8ca60f3a (MD5) Previous issue date: 2015-08-03 / The complex nature of investment decisions, which involves a wide range of factors, results that the decision maker rarely dominates all aspects that determine the quality of his/her decision. Decisions are made by individuals and their personal preferences may influence the outcome of their decision. Investment evaluation processes of an organization should help to ensure the manager that investment projects are analyzed according to a welldefined evaluation guide, in the same way and with due impartiality, in order to avoid that such personal preferences outweigh the "canons" of the organization. There are within the theory the necessary and sufficient tools for the investment evaluation, however, with regard to guides for investment evaluation, these are developed by the own organizations to meet their specific needs. In this study, an exploratory research seeks to conceptualize investment project and investment evaluation. Discounted cash flow methodology, capital asset pricing model and traditional metrics of investment evaluation are studied, as well as the aspects related to risk, uncertainty and irreversibility of real projects. Then, having as objective to extract from the theory an investment evaluation guide based on discounted cash flow methodology, several guides for evaluating investments were analyzed, looking for a systematization of procedures able to gather the essential steps of an evaluation, to ensure that these are carried out systematically. Afterwards, based on a case study, the resulting investment evaluation guide has been tested in a real case - previously assessed by a renowned consulting firm - and it has been confirmed that the said evaluation guide could have been successfully employed to evaluate the real case / A natureza complexa das decisões de investimento, por envolver um largo espectro de fatores, resulta em que raramente o gestor domina todos os aspectos determinantes da qualidade de sua decisão. As decisões são tomadas por indivíduos e suas preferências pessoais podem influenciar o resultado de sua decisão. Os processos de avaliação de investimento de uma organização deveriam contribuir para assegurar ao gestor que os projetos de investimento fossem analisados segundo um roteiro de avaliação bem definido, da mesma maneira e com a devida imparcialidade, de forma a evitar que tais preferências pessoais pudessem sobrepujar os cânones da organização. Encontram-se na teoria as ferramentas necessárias e suficientes para a avaliação de investimento, entretanto, no que concerne aos roteiros para avaliação de investimento, estes são desenvolvidos pelas próprias organizações para atender às suas necessidades específicas. Nesse estudo, por meio de uma pesquisa exploratória, procurou-se conceituar projeto de investimento e avaliação de investimento, estudou-se metodologia de fluxo de caixa descontado, metodologia de precificação de capitais, as métricas tradicionais de avaliação de investimento, e os aspectos relativos ao risco, incerteza e irreversibilidade dos projetos reais. Em seguida, tendo como objetivo extrair da teoria um roteiro para avaliação de investimentos com base na metodologia de fluxo de caixa descontado, vários guias de avaliação de investimentos foram analisados, em busca de uma sistematização de procedimentos capaz de reunir as etapas essenciais de uma avaliação, de forma a assegurar que essas sejam levadas a cabo, sistematicamente. Depois, com base em um estudo de caso, o guia de avaliação de investimento resultante do estudo foi testado em um caso real previamente avaliado por uma empresa de consultoria de renome e confirmou-se que o referido guia de avaliação poderia ter sido empregado com sucesso para avaliar o caso real

Since the turn of the millennium, a large number of computational models of visual salience have been put forward. How best to evaluate a given model's ability to predict where human observers fixate in images of real-world scenes remains an open research question. Assessing the role of spatial biases is a challenging issue; this is particularly true when we consider the tendency for high-salience items to appear in the image center, combined with a tendency to look straight ahead (“central bias”). This problem is further exacerbated in the context of model comparisons, because some—but not all—models implicitly or explicitly incorporate a center preference to improve performance. To address this and other issues, we propose to combine a-priori parcellation of scenes with generalized linear mixed models (GLMM), building upon previous work. With this method, we can explicitly model the central bias of fixation by including a central-bias predictor in the GLMM. A second predictor captures how well the saliency model predicts human fixations, above and beyond the central bias. By-subject and by-item random effects account for individual differences and differences across scene items, respectively. Moreover, we can directly assess whether a given saliency model performs significantly better than others. In this article, we describe the data processing steps required by our analysis approach. In addition, we demonstrate the GLMM analyses by evaluating the performance of different saliency models on a new eye-tracking corpus. To facilitate the application of our method, we make the open-source Python toolbox “GridFix” available.

Time series anomaly detection has been a longstanding area of research with applications across various domains. In recent years, there has been a surge of interest in applying deep learning models to this problem domain. This thesis presents a critical examination of the efficacy of deep learning models in comparison to classical approaches for time series anomaly detection. Contrary to the widespread belief in the superiority of deep learning models, our research findings suggest that their performance may be misleading and the progress illusory. Through rigorous experimentation and evaluation, we reveal that classical models outperform deep learning counterparts in various scenarios, challenging the prevailing assumptions. In addition to model performance, our study delves into the intricacies of evaluation metrics commonly employed in time series anomaly detection. We uncover how it inadvertently inflates the performance scores of models, potentially leading to misleading conclusions. By identifying and addressing these issues, our research contributes to providing valuable insights for researchers, practitioners, and decision-makers in the field of time series anomaly detection, encouraging a critical reevaluation of the role of deep learning models and the metrics used to assess their performance. / Tidsperiods avvikelsedetektering har varit ett långvarigt forskningsområde med tillämpningar inom olika områden. Under de senaste åren har det uppstått ett ökat intresse för att tillämpa djupinlärningsmodeller på detta problemområde. Denna avhandling presenterar en kritisk granskning av djupinlärningsmodellers effektivitet jämfört med klassiska metoder för tidsperiods avvikelsedetektering. I motsats till den allmänna övertygelsen om överlägsenheten hos djupinlärningsmodeller tyder våra forskningsresultat på att deras prestanda kan vara vilseledande och framsteg illusoriskt. Genom rigorös experimentell utvärdering avslöjar vi att klassiska modeller överträffar djupinlärningsalternativ i olika scenarier och därmed utmanar de rådande antagandena. Utöver modellprestanda går vår studie in på detaljerna kring utvärderings-metoder som oftast används inom tidsperiods avvikelsedetektering. Vi avslöjar hur dessa oavsiktligt överdriver modellernas prestandapoäng och kan därmed leda till vilseledande slutsatser. Genom att identifiera och åtgärda dessa problem bidrar vår forskning till att erbjuda värdefulla insikter för forskare, praktiker och beslutsfattare inom området tidsperiods avvikelsedetektering, och uppmanar till en kritisk omvärdering av djupinlärningsmodellers roll och de metoder som används för att bedöma deras prestanda.