In this paper two different models for forecasting the number of monthly departing passengers from Sweden to any international destination are developed and compared. The Swedish transport agency produces forecasts on a yearly basis, where net export is the only explanatory variable controlled for in the latest report. More profound studies have shown a relevance of controlling for variables such as unemployment rate, oil price and exchange rates. Due to the high seasonality within passenger flows, these forecasts are based on monthly or quarterly data. This paper shows that a seasonal autoregressive integrated moving average model with exogenous input outperforms the benchmark model forecast in seven out of nine months. Thus, controlling for oil price, the SEK/EUR exchange rate and the occurrence of Easter reduces the mean absolute percentage error of the forecasts from 3,27 to 2,83 % on Swedish data.

Time series modeling is a commonly used approach in exchange for studying and analyzing the data to support decision-making in companies based on historical data and thereby help them to save costs. This work introduces a forecasting framework that utilizes a seasonal autoregressive integrated moving average with exogenous variables (SARIMAX) model to forecast the number of people expected to enter a building within a short period. We applied the model to forecast the abovementioned value at California University Irvine's main door using an open-source dataset that comprised data spanning four months. The experimental results demonstrate that the SARIMAX model exhibits encouraging performance in classification andevaluation, as evidenced by the promising results. The RMSE values for one,two, three, and four prediction weeks are 24.6, 40.4, 36, and 38.7, respectively, accompanied by corresponding percentage errors of 2%, 4.8%,4.76%, and 1.01%. These metrics highlight the model's ability to predict outcomes accurately and indicate its effectiveness in forecasting over various time horizons. Furthermore, the proposed model addresses the issue of inadequate future planning and analyzes foot traffic to provide a reliable forecasting technique, which is essential for modern building facilities management.

Huvudsyftet med denna studie är att förstå om ett säsongsmässigt autoregressivt integrerat rörligt genomsnitt (SARIMA) -metod pålitligt kan förutsäga extrem variation i värmelaster för en fjärrvärmestation. Genom extrem variation ser vi på den maximala och minsta värmebelastningen per dag mätt i megawattstimmar. Avhandlingen bygger på standardimplementering av SARIMAX och utför en rutnätsökning efter de mest lämpliga parametrarna. Prognoser kan genereras från tidsserier i syfte att uppskatta förväntad energiförbrukning i en fjärrvärmestation. Frågan som ställs är: Hur tillförlitlig är SARIMAX-modellen för energibehov i en fjärrvärmestation? För att besvara studiens fråga designas och genomförs experiment med hjälp av ett dataset från verkliga mätningar. Datasetet studerades och analyserades med hjälp av undersökande dataanalystekniker som kommer med statistiska paket implementerade i en pythonmiljön, som kan användas som ett statistiskt program. Uppgifterna är uppdelade i två säsonger, sommar och vinter. Där den explorativa analysen av datasetet visar att modellen måste ta hänsyn till den starka veckocykeln med data. Så att korrelationen mellan utetemperaturen kan användas för att förbättra förutsägelsen. Fininställning och tillämpning av SARIMAX och Prophet för förutsägelser genererar data i form av diagram som visar hur tillförlitlig modellen är för förutsägelse. Resultaten visar att SARIMAX-modellen presterar bättre under vintermånaderna och sämre under sommaren. Baserat på dessa resultat antyder avhandlingsstudien att SARIMAX-modellen är mer tillämplig under vintermånaderna där förutsägelsen är mer tillförlitlig. Jämförelser med Prophet modellen indikerar lovande resultat och att vidare forskning borde föras för denna modell. Dessa resultat kan vara till hjälp för industrin som förser samhället och konsumenterna med fjärrvärme. Det hjälper till att förutse hur mycket energiförbrukning som används där industrin kan använda den för att reglera mängden fjärrvärme, för att ytterligare hjälpa ekonomin och miljön. / The main objective in this study is to understand if a Seasonal Autoregressive Integrated Moving Average (SARIMA) method can reliably predict extreme variation in heat loads for a district heating substation. By extreme variation we look at the maximum and minimum heat load per day measured in megawatt hour. The thesis relies on standard implementation of SARIMAX and performs a grid search for the most suitable parameters. Forecast can be generated from time series with the purpose of estimating expected energy consumption in a district heating substation. The question addressed is: How reliable is the SARIMAX-model for energy demand in a district heating substation? To answer the study’s question, experiments are designed and conducted using a dataset from real measurements. The dataset was studied and analyzed using exploratory data analysis techniques that come with statistical packages implemented in the python environment, which can be used as a statistical program. The data is separated into two seasons, summer and winter. Where the explorative analysis of the data shows that the model needs to take in account the strong weekly cycle of data. Also the correlation between the outside temperature can be used to improve prediction. Fine tuning and applying SARIMAX and Prophet for predictions generates data in the form of graphs and tables which shows how reliable the SARIMAX model is for prediction. Results show that the SARIMAX model is performing better during winter months and worse during summer. Based on these results, the thesis study suggests that the SARIMAX-model is more applicable during winter months where prediction is more reliable. Comparison with the Prophet model indicates promising results and that further investigations should be made into this model. These results can be of help to the industry that supplies the community and consumers with district heating. It helps by predicting how much energy consumption is used where the industry can use it to regulate the amount of district heating, to further help the economy and environment.

Financial inclusion is a public policy objective that fosters development through access to financial services for all. Financial inclusion can be defined as access, usage and quality of financial services. Inclusion of individuals and small enterprises has made considerable progress but it has also reached excesses in some situations. Regulatory changes and technological innovation have helped the expansion of financial services. Our contribution to the literature is threefold. First, we expand the large body of research that focuses on financial inclusion based on access to credit, through our analysis of payments. We provide an unique analysis of the quality dimension of payments, which we define as a catalyst between the access and usage dimensions. Second, we provide a detailed analysis of the Brazilian payment market, which transacts close to $400bn per year, in the scarce literature on developing countries. Third, we isolate the determinants of electronic payments through statistical methods, including a principal component analysis and auto regressive models (SARIMA, SARIMAX), which have not yet been used by researchers. We find that four macro characteristicshave a strong explanatory power: bank credit card lending, active population, retail sales and cash-in-circulation. Suprisingly, we find that cash-in-circulation presents a positive relationship with electronic payments, suggesting a possible distrust of citizens towards the banking system, high levels of informality, and shedding a new light on the precautionary principle described by Keynes. Our analysis is based on monthly deflated card payment data for Brazil from January 2007 to March 2017. / A Inclusão financeira é um objetivo de política pública que procura desenvolvimento através do acesso de todos aos serviços financeiros. Esse conceito pode ser definido com as suas três dimensões de acesso, uso e qualidade dos serviços. A inclusão de indivíduos e empresas conheceu uma melhora significativa, e em algum casos, apresentou excessos. Adaptações regulatórias e inovação tecnológica serviram de pano de fundo para a inclusão. Apresentamos as três contribuições da nossa pesquisa. Primeiro, existe ampla literatura sobre inclusão financeira com foco em crédito, e apresentamos um estudo original sobre pagamentos e sua dimensão de qualidade, definida como o catalisador entre acesso e uso. Segundo: nossa pesquisa apresenta uma análise única do setor de pagamentos no Brasil, um setor com faturamento de mais de R$1.2 trilhões de reais anuais, no âmbito da escassa literatura sobre economias em desenvolvimento. A terceira contribuição apresenta os determinantes dos meios de pagamentos eletrônicos, usando modelos estatísticos originais, como componentes principais e modelos auto regressivos (SARIMA, SARIMAX), que não tinham sido usados na literatura de inclusão financeira. Identificamos quatro características com significância para explicar meios eletrônicos: crédito bancário, população ativa, vendas do varejo e dinheiro em posse das famílias. De maneira surpreendente, dinheiro em posse das famílias apresentou correlação positiva com meios eletrônicos, sinalizando uma desconfiança dos consumidores com o setor bancário ou um maior grau de informalidade da economia brasileira, e trazendo uma interpretação original ao princípio de precaução descrito por Keynes. Nossa pesquisa se baseou em dados agregados e deflacionados de pagamentos para o Brasil entre Janeiro de 2007 e Março de 2017.

Esta dissertação testa e compara dois tipos de modelagem para previsão de uma mesma série temporal. Foi observada uma série temporal de distribuição de energia elétrica e, como estudo de caso, optou-se pela região metropolitana do Estado da Bahia. Foram testadas as combinações de três variáveis exógenas em cada modelo: a quantidade de clientes ligados na rede de distribuição de energia elétrica, a temperatura ambiente e a precipitação de chuvas. O modelo linear de previsão de séries temporais utilizado foi um SARIMAX. A modelagem de inteligência computacional utilizada para a previsão da série temporal foi um sistema de Inferência Fuzzy. Na busca de um melhor desempenho, foram feitos testes de quais variáveis exógenas melhor influenciam no comportamento da energia distribuída em cada modelo. Segundo a avaliação dos testes, o sistema Fuzzy de previsão foi o que obteve o menor erro. Porém dentre os menores erros, os resultados dos testes também indicaram diferentes variáveis exógenas para cada modelo de previsão. / This dissertation tests and compares two types of predicting models to the same time series. A time series of electricity distribution was observed and, as a case study, were opted for the metropolitan region of Bahia State. Three exogenous variables were tested in each model: the number of customers connected to the electricity distribution network, the temperature and the precipitation of rain. The linear model time series forecasting used was a SARIMAX. The modelling of computational intelligence used to predict the time series was a Fuzzy Inference System. For better performance, in each model was tested all the exogenous variables to fit the influence in the energy distributed. According to the evaluation of the tests, the Fuzzy forecasting system presented the lowest error. But among the smallest errors, the results of the tests also indicated different exogenous variables for each forecast model.

Esta dissertação testa e compara dois tipos de modelagem para previsão de uma mesma série temporal. Foi observada uma série temporal de distribuição de energia elétrica e, como estudo de caso, optou-se pela região metropolitana do Estado da Bahia. Foram testadas as combinações de três variáveis exógenas em cada modelo: a quantidade de clientes ligados na rede de distribuição de energia elétrica, a temperatura ambiente e a precipitação de chuvas. O modelo linear de previsão de séries temporais utilizado foi um SARIMAX. A modelagem de inteligência computacional utilizada para a previsão da série temporal foi um sistema de Inferência Fuzzy. Na busca de um melhor desempenho, foram feitos testes de quais variáveis exógenas melhor influenciam no comportamento da energia distribuída em cada modelo. Segundo a avaliação dos testes, o sistema Fuzzy de previsão foi o que obteve o menor erro. Porém dentre os menores erros, os resultados dos testes também indicaram diferentes variáveis exógenas para cada modelo de previsão. / This dissertation tests and compares two types of predicting models to the same time series. A time series of electricity distribution was observed and, as a case study, were opted for the metropolitan region of Bahia State. Three exogenous variables were tested in each model: the number of customers connected to the electricity distribution network, the temperature and the precipitation of rain. The linear model time series forecasting used was a SARIMAX. The modelling of computational intelligence used to predict the time series was a Fuzzy Inference System. For better performance, in each model was tested all the exogenous variables to fit the influence in the energy distributed. According to the evaluation of the tests, the Fuzzy forecasting system presented the lowest error. But among the smallest errors, the results of the tests also indicated different exogenous variables for each forecast model.

Financial inclusion is a public policy objective that fosters development through access to financial services for all. Financial inclusion can be defined as access, usage and quality of financial services. Inclusion of individuals and small enterprises has made considerable progress but it has also reached excesses in some situations. Regulatory changes and technological innovation have helped the expansion of financial services. Our contribution to the literature is threefold. First, we expand the large body of research that focuses on financial inclusion based on access to credit, through our analysis of payments. We provide an unique analysis of the quality dimension of payments, which we define as a catalyst between the access and usage dimensions. Second, we provide a detailed analysis of the Brazilian payment market, which transacts close to $400bn per year, in the scarce literature on developing countries. Third, we isolate the determinants of electronic payments through statistical methods, including a principal component analysis and auto regressive models (SARIMA, SARIMAX), which have not yet been used by researchers. We find that four macro characteristicshave a strong explanatory power: bank credit card lending, active population, retail sales and cash-in-circulation. Suprisingly, we find that cash-in-circulation presents a positive relationship with electronic payments, suggesting a possible distrust of citizens towards the banking system, high levels of informality, and shedding a new light on the precautionary principle described by Keynes. Our analysis is based on monthly deflated card payment data for Brazil from January 2007 to March 2017. / A Inclusão financeira é um objetivo de política pública que procura desenvolvimento através do acesso de todos aos serviços financeiros. Esse conceito pode ser definido com as suas três dimensões de acesso, uso e qualidade dos serviços. A inclusão de indivíduos e empresas conheceu uma melhora significativa, e em algum casos, apresentou excessos. Adaptações regulatórias e inovação tecnológica serviram de pano de fundo para a inclusão. Apresentamos as três contribuições da nossa pesquisa. Primeiro, existe ampla literatura sobre inclusão financeira com foco em crédito, e apresentamos um estudo original sobre pagamentos e sua dimensão de qualidade, definida como o catalisador entre acesso e uso. Segundo: nossa pesquisa apresenta uma análise única do setor de pagamentos no Brasil, um setor com faturamento de mais de R$1.2 trilhões de reais anuais, no âmbito da escassa literatura sobre economias em desenvolvimento. A terceira contribuição apresenta os determinantes dos meios de pagamentos eletrônicos, usando modelos estatísticos originais, como componentes principais e modelos auto regressivos (SARIMA, SARIMAX), que não tinham sido usados na literatura de inclusão financeira. Identificamos quatro características com significância para explicar meios eletrônicos: crédito bancário, população ativa, vendas do varejo e dinheiro em posse das famílias. De maneira surpreendente, dinheiro em posse das famílias apresentou correlação positiva com meios eletrônicos, sinalizando uma desconfiança dos consumidores com o setor bancário ou um maior grau de informalidade da economia brasileira, e trazendo uma interpretação original ao princípio de precaução descrito por Keynes. Nossa pesquisa se baseou em dados agregados e deflacionados de pagamentos para o Brasil entre Janeiro de 2007 e Março de 2017.

Accurate day-ahead grid loss forecasts are, among other things, essential to determine the electricity price for the upcoming day. The more accurate forecast, the closer the trading on the 'day-ahead' electricity market can become the actual operation the next day, which dedcrease the need for correcting production on the balancing market. Followingly, the need for extra imbalance costs, which make the electricity price higher, is reduced with accurate forecasts. This project's purpose was to explore a wide range of mathematical models to increase the energy comapny Fortum's day-ahead grid loss forecasting accuracy, and thereby contribute to lower the risk for high imbalance costs.  Two electrical grids located in Sweden, with different characteristics, were studied. One electrical grid was located in Dalarna and the other one was located in Värmland. Four different model types were tested for each grid. The linear models ARIMAX and SARIMAX were explored and the two artificial neural networks FNN (Feedforward Neural Network) and LSTM-RNN (Long-SHort Term Memory Recurrent Neural Network) were explored. By constructing different model structures of each model type, as well as statistically testing which predictors to include as input to the models, the most accurate model for grid loss forecasting was found. The models' forecasting accuracy were validated based on the MAPE (Mean Absolute Percentage Error). Variables important as predictors were found to be power production, electricity prices and grid losses at previous time steps. For the grid in Dalarna ARIMAX(2,0,2) was the model generating most accurate day-ahead grid loss forecasts and for the grid in Värmland, SARIMAX(1,0,0)(0,0,1)[24] was the most accurate model. That is, different models were found as the most accurate one for grid loss foracsting, as the two studied electricity grids had different characteristics. Hence, this result implies that there is no universal model that is the most adequate at modelling all types of grid losses. To find useful models when forecasting grid losses day-ahead, an analysis of the particular grid losses being studied is therefore not irrelevant.

La Tension dans un Service d’Urgences (SU) est un déséquilibre entre le flux de charge des soins et la capacité de prise en charge sur une durée suffisante pouvant entrainer des conséquences néfastes au bon fonctionnement. Elle se reflète par la surcharge des locaux, l’allongement des délais de traitement et d’attente. Ce qui provoque à la fois l’insatisfaction des patients et l’anxiété du personnel. Cette thèse s’inscrit dans le cadre du projet HOST financé par le programme ANR-TECSAN-2011 afin d’élaborer un Système d'Aide à la Gestion de la Tension (SAGeT) assurant trois objectifs:1. L’évaluation multicritère grâce à une panoplie d’indicateurs agrégés par la logique floue afin de résoudre la subjectivité du ressentie humain de la tension. Chaque scénario d’évaluation déclenche des règles de décision spécifiques ciblant ainsi des points de défaillance à surveiller.2. L’anticipation de la demande sur différents horizons temporels : l’application des méthodes SARIMA et SARIMAX est justifiée par la saisonnalité des chroniques de visites et l’influence de certains paramètres externes (épidémies, vacances, météo). De plus, la qualité de l’information venant de l’historique a été améliorée par une recomposition d’historique basée sur la vraisemblance journalière.3. L’amélioration de la gestion des flux et le pilotage de l’activité puisque l’utilisation de SAGeT comme un tableau de bord offre une vue macro sur l’ensemble de l’activité (lits occupés, patients en attente, durées de passages prévisionnelles et allongements excessifs). Les simulations traitent des vrais scénarios de tension observés entre 2011 et 2013 dans le SU Pédiatriques Jeanne de Flandre du CHRU-Lille. / He strain in an Emergency Department (ED) is an imbalance between the total demand load of healthcare treatment and resources ability to support it during a convenient horizon, which may results negative consequences on the smooth running of the activity. It is reflected by overcrowding, longer treatment and waiting times which causes both patients dissatisfaction and anxiety of personnel. This thesis is part of the HOST project funded by the ANR-TECSAN-2011 program to develop a Management Support System of Strain (MSSS) ensuring three objectives:1. Multi-criteria evaluation through a variety of indicators aggregated by fuzzy logic to solve the subjectivity of the human feeling of strain. Each evaluation scenario involves specific decision rules targeting to supervise failure points.2. Demand forecasting through several time horizons: applying SARIMA and SARIMAX methods is justified by the time series seasonality of visits and the influence of some external parameters (epidemics, holidays, weather). In addition, the quality of the historical information has been improved by a history rebuilding based on the daily likelihood.3. Improving flow management and activity monitoring since the use of MSSS as a dashboard provides a macro view of the whole activity (beds occupied, waiting, estimated length of stay, excessive elongation).The simulations address real strain scenarios observed between 2011 and 2013 in the Pediatric ED Jeanne de Flandre of the Regional University Hospital of Lille (France).

Since the 2008 financial crisis, the interest for the subject area of modelling non-maturity deposits has been growing quickly. The area has been widely analysed from the perspective of a traditional bank where customers foremost have transactional and salary deposits. However, in recent year the Swedish banking sector has become more digitized. This has opened up opportunities for more niche banking actors to establish themselves on the market. Therefore, this study aims to examine how the theories developed and previously used in modelling liquidity volumes at traditional banks can be used at a niche bank focused on savings and investments. In this study the topics covered are short-rate modelling using Vasicek's model, liquidity volume modelling using SARIMA and SARIMAX modelling as well as liquidity risk modelling using an approach developed by Kalkbrener and Willing. When modelling the liquidity volumes the data set was divided depending on account and customer type into six groups, for four out of these the models had lower in and out of set prediction errors using SARIMA models for only two of the six models were there improvements made to the in and out of set prediction error using SARIMAX models. Finally, the resulting minimization of liquidity volume forecasting 5 years in the future gave reasonable and satisfactory results. / Sedan finanskrisen 2008 har intresset kring ämnesområdet gällande modellering av inlåningsvolymer utan en kontrakterad förfallodag ökat snabbt. Området har analyserats i stor utsträckning från perspektivet av en traditionell bank där kunder har framförallt transaktions- och lönekonton. De senaste åren har den Svenska banksektorn blivit mer digitaliserad. Detta har öppnat upp möjligheter för nischbanker att etablera sig på marknaden. Därför ämnar denna studie att undersöka hur teorier som har utvecklats och tidigare använts på traditionella banker för att modellera likviditetsvolymer kan användas på en nischbank som är fokuserad på sparande och investeringar. I denna studie modelleras korträntor med Vasicek's modell, likviditetsvolymer med SARIMA och SARIMAX modeller och likviditetsrisk med en modell utvecklad av Kalkbrener och Willing. För modelleringen av likviditetsvolymer delades likviditetsdatan upp i sex grupper baserat på konto- och kund typ. För fyra av dessa data set gav SARIMA-modeller lägre prediktionsfel och endast för två av de sex grupperna gav SARIMAX-modeller bättre resultat. Slutligen så gav den resulterande minimeringen av nödvändiga likviditetsvolymer på en 5 årig horisont rimliga och tillfredsställande resultat.