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Vasaloppet is a cross country ski event which has been held in Sweden for about 50 years. Now more than 50,000 people of different ages participate in various cross country ski races during the Vasaloppet winter week in Dalarna County. This increasing demand needs good traffic and transportation planning to avoid congestion and provide safe, on time and environmentally friendly transportation for participants and visitors to the area. The key for a good event traffic planning is reliable and up-to-date traffic data which is not available for the Vasaloppet winter week. This study is an attempt to collect traffic data in order to find the movement patterns in the area and estimate origin-destination matrices for the main event of Vasaloppet week. Based on resources and time limitation it was decided to use a web-base d participants’ survey in order to collect traffic data. The link to the survey was sent to email address of a sample of 5000 participants. About 64% of the participants drove from their home town to the area and about 31 percent travelled by bus. Train and airplane have a very small share in travel mode to the area. Malungsälen, Mora and Älvdalen are three municipalities in Dalarna County with the highest share in accommodating participants. On the day of the race, bus and car have approximately the same share in travel mode with 45% and 47% respectively.

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This thesis develops and evaluates a probabilistic model that estimates the stoppage time in football. Stoppage time represents the additional minutes of play given after the original matchtime is over. It is crucial in determining the course of events during the remainder of a match, thereby affecting the odds of live sports betting. The proposed approach uses the negative binomial distribution to model events in football and stoppage time is viewed as a convolution of these distributions. The parameters of the negative binomials are estimated using machine learning methods in Python, with TensorFlow as the underlying framework. The data used for the analysis consists of event data for thousands of football matches with corresponding stoppage time, as well as the duration of pauses that have occurred in these games. The negative binomial distribution is shown to be a good fit and can be adapted to the data using scaling and resolution techniques. The model allows us to see how different events contribute to the stoppage time, and the results indicate that injuries, VAR checks, and red cards have the most significant impact on stoppage time. The model has potential for use in live sports betting and can enhance the accuracy of odds calculation. This work was conducted in collaboration with xAlgo which is a department of Kambi, a business-to-business provider of sports betting services.

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During the COVID-19 pandemic, customer shopping habits have changed. Some industries experienced an abrupt shift during the pandemic outbreak while others navigate in new normal states. For some merchants, the highly-uncertain new phenomena of COVID-19 expresses as outliers in time series of volume of sales. As forecasting models tend to replicate past behavior of a series, outliers complicates the procedure of forecasting; the abnormal events tend to unreliably replicate in forecasts of the subsequent year(s). In this thesis, we investigate how to forecast volume of sales during the abnormal time period of COVID-19, where the classical ARIMA family of models produce unreliable forecasts. The research revolved around three time series exhibiting three types of outliers: a level shift, a transient change and an additive outlier. Upon detecting the time period of the abnormal behavior in each series, two experiments were carried out as attempts for increasing the predictive accuracy for the three extreme cases. The first experiment was related to imputing the abnormal data in the series and the second was related to using a combined model of a pre-pandemic and a post-abnormal forecast. The results of the experiments pointed at significant improvement of the mean absolute percentage error at significance level alpha=0.05 for the level shift when using a combined model compared to the pre-pandemic best-fit SARIMA model. Also, at significant improvement for the additive outlier when using a linear impute. For the transient change, the results pointed at no significant improvement in the predictive accuracy of the experimental models compared to the pre-pandemic best-fit SARIMA model. For the purpose of generalizing to large-scale conclusions of methods' superiority or feasibility for particular abnormal behaviors, empirical evaluations are required. The proposed experimental models were discussed in terms of reliability, validity and quality. By residual diagnostics, it was argued that the models were valid; however, that further improvements can be made. Also, it was argued that the models fulfilled desired attributes of simplicity, scaleability and flexibility. Due to the uncertain phenomena of the COVID-19 pandemic, it was suggested not to take the outputs as long-term reliable solutions. Rather, as temporary solutions requiring more frequent updating of forecasts. / Under coronapandemin har kundbeteenden och köpvanor förändrats. I vissa branscher upplevdes ett plötsligt skifte vid pandemiutbrottet och i andra navigerar handlare i nya normaltillstånd. För vissa handlare är förändringarna så pass distinkta att de yttrar sig som avvikelser i tidsserier över försäljningsvolym. Dessa avvikelser komplicerar prognosering. Då prognosmodeller tenderar att replikera tidsseriers tidigare beteenden, tenderas det avvikande beteendet att replikeras i försäljningsprognoser för nästkommande år. I detta examensarbete ämnar vi att undersöka tillvägagångssätt för att estimera försäljningsprognoser under den abnorma tidsperioden av COVID-19, då klassiska tidsseriemodeller felprognoserar. Detta arbete kretsade kring tre tidsserier som uttryckte tre avvikelsertyper: en nivåförskjutning, en övergående förändring och en additiv avvikelse. Efter att ha definierat en specifik tidsperiod relaterat till det abnorma beteendet i varje tidsserie, utfördes två experiment med syftet att öka den prediktiva noggrannheten för de tre extremfallen. Det första experimentet handlade om att ersätta den abnorma datan i varje serie och det andra experimentet handlade om att använda en kombinerad pronosmodell av två estimerade prognoser, en pre-pandemisk och en post-abnorm. Resultaten av experimenten pekade på signifikant förbättring av ett absolut procentuellt genomsnittsfel för nivåförskjutningen vid användande av den kombinerade modellen, i jämförelse med den pre-pandemiskt bäst passande SARIMA-modellen. Även, signifikant förbättring för den additiva avvikelsen vid ersättning av abnorm data till ett motsvarande linjärt polynom. För den övergående förändringen pekade resultaten inte på en signifikant förbättring vid användande av de experimentella modellerna. För att generalisera till storskaliga slutsatser giltiga för specifika avvikande beteenden krävs empirisk utvärdering. De föreslagna modellerna diskuterades utifrån tillförlitlighet, validitet och kvalitet. Modellerna uppfyllde önskvärda kvalitativa attribut såsom enkelhet, skalbarhet och flexibilitet. På grund av hög osäkerhet i den nuvarande abnorma tidsperioden av coronapandemin, föreslogs det att inte se prognoserna som långsiktigt pålitliga lösningar, utan snarare som tillfälliga tillvägagångssätt som regelbundet kräver om-prognosering.

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A scenario in which modern machine learning models are trained is to make use of past data to be able to make predictions about the future. When working with multiple structured and time-labeled datasets, it has become a more common practice to make use of a join operator called the Point-in-Time join, or PIT join, to construct these datasets. The PIT join matches entries from the left dataset with entries of the right dataset where the matched entry is the row whose recorded event time is the closest to the left row’s timestamp, out of all the right entries whose event time occurred before or at the same time of the left event time. This feature has long only been a part of time series data processing tools but has recently received a new wave of attention due to the rise of the popularity of feature stores. To be able to perform such an operation when dealing with a large amount of data, data engineers commonly turn to large-scale data processing tools, such as Apache Spark. However, Spark does not have a native implementation when performing these joins and there has not been a clear consensus by the community on how this should be achieved. This, along with previous implementations of the PIT join, raises the question: ”How to perform fast and resource efficient Pointin- Time joins in Apache Spark?”. To answer this question, three different algorithms have been developed and compared for performing a PIT join in Spark in terms of resource consumption and execution time. These algorithms were benchmarked using generated datasets using varying physical partitions and sorting structures. Furthermore, the scalability of the algorithms was tested by running the algorithms on Apache Spark clusters of varying sizes. The results received from the benchmarks showed that the best measurements were achieved by performing the join using Early Stop Sort-Merge Join, a modified version of the regular Sort-Merge Join native to Spark. The best performing datasets were the datasets that were sorted by timestamp and primary key, ascending or descending, using a suitable number of physical partitions. Using this new information gathered by this project, data engineers have been provided with general guidelines to optimize their data processing pipelines to be able to perform more resource-efficient and faster PIT joins. / Ett vanligt scenario för maskininlärning är att träna modeller på tidigare observerad data för att för att ge förutsägelser om framtiden. När man jobbar med ett flertal strukturerade och tidsmärkta dataset har det blivit vanligare att använda sig av en join-operator som kallas Point-in-Time join, eller PIT join, för att konstruera dessa datauppsättningar. En PIT join matchar rader från det vänstra datasetet med rader i det högra datasetet där den matchade raden är den raden vars registrerade händelsetid är närmaste den vänstra raden händelsetid, av alla rader i det högra datasetet vars händelsetid inträffade före eller samtidigt som den vänstra händelsetiden. Denna funktionalitet har länge bara varit en del av datahanteringsverktyg för tidsbaserad data, men har nyligen fått en ökat popularitet på grund av det ökande intresset för feature stores. För att kunna utföra en sådan operation vid hantering av stora mängder data vänder sig data engineers vanligvis till storskaliga databehandlingsverktyg, såsom Apache Spark. Spark har dock ingen inbyggd implementation för denna join-operation, och det finns inte ett tydligt konsensus från Spark-rörelsen om hur det ska uppnås. Detta, tillsammans med de tidigare implementationerna av PIT joins, väcker frågan: ”Vad är det mest effektiva sättet att utföra en PIT join i Apache Spark?”. För att svara på denna fråga har tre olika algoritmer utvecklats och jämförts med hänsyn till resursförbrukning och exekveringstid. För att jämföra algoritmerna, exekverades de på genererade datauppsättningar med olika fysiska partitioner och sorteringstrukturer. Dessutom testades skalbarheten av algoritmerna genom att köra de på Spark-kluster av varierande storlek. Resultaten visade att de bästa mätvärdena uppnåddes genom att utföra operationen med algoritmen early stop sort-merge join, en modifierad version av den vanliga sort-merge join som är inbyggd i Spark, med en datauppsättning som är sorterad på tidsstämpel och primärnyckel, antingen stigande eller fallande. Fysisk partitionering av data kunde även ge bättre resultat, men det optimala antal fysiska partitioner kan variera beroende på datan i sig. Med hjälp av denna nya information som samlats in av detta projekt har data engineers försetts med allmänna riktlinjer för att optimera sina databehandlings-pipelines för att kunna utföra mer resurseffektiva och snabbare PIT joins

In this work the relationships between penetrant structure, its transport properties, and its effects on the mechanical properties of a polymer matrix were investigated. Although there is a vast amount of data on the diffusion of low molecular weight molecules into polymeric materials and on the mechanical properties of various polymer-penetrant systems, no attempts have been made to inter-relate the two properties with respect to the chemical structure of the diffusant. Therefore, two series of penetrants - n-alkanes and esters - were examined in this context, with the goal of correlating molecular size, shape, and chemical nature of the penetrant to its final transport and matrix mechanical properties. These correlations have been demonstrated to allow quantitative prediction of one property, given a reasonable set of data on the other parameters. A series of n-alkanes (C6-C17) and esters (C5-C17) have been used to separate the effects of penetrant size and shape, from those due to polymer-penetrant interactions, in the diffusion through a polyamide polymeric adhesive. These effects have been taken into account in order to yield a qualitative relationship that allows for prediction of diffusivity based upon penetrant structural information. Transport properties have been analyzed using mass uptake experiments as well as an in-situ FTIR-ATR technique to provide detailed kinetic as well as thermodynamic information on this process. The phenomenon of diffusion and its effects on the resulting dynamic mechanical response of a matrix polymeric adhesive have been studied in great detail using the method of reduced variables. The concept of a diffusion-time shift factor (log aDt) has been introduced to create doubly-reduced master curves, taking into account the effects of temperature and the variations in the polymer mechanical response due to the existence of a low molecular weight penetrant. / Ph. D.

In this series of essays, we study the influence of weight and group size in the sequential multi-battle contest with laboratory experiences (Chapter 2 and Chapter 3). We then develop an empirical method to model perceptual present and time inconsistency (Chapter 4). Chapter 2 examines how the weight and the ordered weights in battles affect the behavior in sequential multi-battle contests with an experiment. We find robustly that the weight of the current battle consistently influences contestants' efforts. Additionally, we discover the math-point-oriented behavior despite differences in history. In other words, the weight effect is expressed in two ways: influencing the effort of the current battle and transferring a contest to the next battle with a designated intensity. Chapter 3 explores the group size effect and how the contest success functions influence the group size effect in sequential multi-battle contests with an experiment. We capture the negative group size effect on the leaders' efforts, participation and dropout rates; contrarily, the positive effect on the non-leaders' efforts. Compared to the Tullock lottery, the all-pay auction intensifies the group size effect of the high effort in the initial battle. It also enlarges the observed group size effects of the effort gaps between the leaders and the non-leaders. Chapter 4 develops the quasi-hyperbolic discounting model into the general beta-delta model to parametrically detect and measure the inconsistency in revealed time preference. This method empirically classifies time preference into four categories, i.e., time consistent, present bias, future bias, and mixed inconsistent. Then we applied this method to the convex time budget data of seven experiments, including 3670 subjects. We discover empirical evidence supporting perceptual differences in the present-future threshold. Traditional present bias models may interpret the time preference imprecisely. / Doctor of Philosophy / Competition and Time are two essential aspects of life. Many decisions are made in a competitive environment. Some other decisions are made when time serves as a critical factor. We divide this dissertation into two parts. In the first part, we study strategic behavior in competitions. Specifically, we examine how (1) the importance of each round (weight), (2) the number of competitors, and (3) the ambiguity of the rule affect the result of a multi-round competition. In the second part, we study people's subjective understanding of time, generally the personal beliefs and preferences of the present and future. In part one (Chapter 2 and Chapter 3), first, we find people are very responsive about the importance of a round in a multi-round competition. When a round is more important, people make more effort in such round. People are also sensible about the competition's current status (leading, behind, or tied) rather than the history. Second, at the beginning of a competition, we find an increase in the participation rate when fewer competitors exist. Suppose there are more competitors; the leading position players compete more brutally; on the contrary, the non-leading players are discouraged more. Third, people spend more energy when the rule is less ambiguous in a multi-round competition. In part two (Chapter 4), We find a very diversified subjective belief in the word "present." The concept of "now" lasts longer than we conventionally thought. When the subjective "present'' is captured at the individual level, we find the immediate now is not necessarily the best way to represent the "average present'' for the population.