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Dissertations / Theses on the topic 'XGBooster'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

Alhomsi, Moaz, and Hinda Ahmed. "Forecasting of ExchangeRate: Autoregressive modelsvs. XGBoost." Thesis, Jönköping University, IHH, Nationalekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-51370.

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In international economics and trading, the exchange rate is important. Forecasting theexchange rate helps in minimizing risks and maximizing profits. The study attempts to test threemodels to forecast EUR/USD exchange rate. Based on previous work by Meese & Rogoff(1983), we replicated the authors work of the Random Walk model of AR(1) on different periodand currency to check if the model was able to forecast the exchange rate. Then we ran ARDLand XGBoost models to find which of the two models performed better than the Random Walkmodel based on different measures. The measures are Root Mea

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Blomqvist, Johanna. "Using XGBoost to classify theBeihang Keystroke Dynamics Database." Thesis, Uppsala universitet, Datalogi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-357420.

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Keystroke Dynamics enable biometric security systems by collecting and analyzing computer keyboard usage data. There are different approaches to classifying keystroke data and a method that has been gaining a lot of attention in the machine learning industry lately is the decision tree framework of XGBoost. XGBoost has won several Kaggle competitions in the last couple of years, but its capacity in the keystroke dynamics field has not yet been widely explored. Therefore, this thesis has attempted to classify the existing Beihang Keystroke Dynamics Database using XGBoost. To do this, keystroke

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3

Salam, Patrous Ziad. "Evaluating XGBoost for User Classification by using Behavioral Features Extracted from Smartphone Sensors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233522.

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Smartphones have opened the possibility to interact with people anytime and anywhere. A significant amount of individuals rely on their smartphone for work-related and everyday tasks. As a consequence modern smartphones include sensitive, valuable and confidential information, such as e-mails, photos, notes, and messages. The primary concern is to prevent unauthorized access to data stored on the smartphone and applications. Traditional authentication methods are entry-point based and do not support continuous authorization. Therefore, as long as the session is active, there are no mechanisms

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4

Elena, Podasca. "Predicting the Movement Direction of OMXS30 Stock Index Using XGBoost and Sentiment Analysis." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-21119.

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Background. Stock market prediction is an active yet challenging research area. A lot of effort has been put in by both academia and practitioners to produce accurate stock market predictions models, in the attempt to maximize investment objectives. Tree-based ensemble machine learning methods such as XGBoost have proven successful in practice. At the same time, there is a growing trend to incorporate multiple data sources in prediction models, such as historical prices and text, in order to achieve superior forecasting performance. However, most applications and research have so far focused o

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Hesselroth, Johannes. "Väderdata, Inverse Distance Weighting och XGBoost för prediktering av väglagsfriktion : En utforskande studie." Thesis, Karlstads universitet, Handelshögskolan (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-74530.

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Henriksson, Erik, and Kristopher Werlinder. "Housing Price Prediction over Countrywide Data : A comparison of XGBoost and Random Forest regressor models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302535.

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The aim of this research project is to investigate how an XGBoost regressor compares to a Random Forest regressor in terms of predictive performance of housing prices with the help of two data sets. The comparison considers training time, inference time and the three evaluation metrics R2, RMSE and MAPE. The data sets are described in detail together with background about the regressor models that are used. The method makes substantial data cleaning of the two data sets, it involves hyperparameter tuning to find optimal parameters and 5foldcrossvalidation in order to achieve good performance e

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Cerna, Ñahuis Selene Leya. "Comparative analysis of XGBoost, MLP and LSTM techniques for the problem of predicting fire brigade Iiterventions /." Ilha Solteira, 2019. http://hdl.handle.net/11449/190740.

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Orientador: Anna Diva Plasencia Lotufo<br>Abstract: Many environmental, economic and societal factors are leading fire brigades to be increasingly solicited, and, as a result, they face an ever-increasing number of interventions, most of the time on constant resource. On the other hand, these interventions are directly related to human activity, which itself is predictable: swimming pool drownings occur in summer while road accidents due to ice storms occur in winter. One solution to improve the response of firefighters on constant resource is therefore to predict their workload, i.e., their n

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Hardin, Maria, and Joakim Wellenstam. "Utveckling av komplexitetsbedömningsmodell med XGBoost för att anpassa text-till-talsyntes inom Robot-Assisted Language Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263693.

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Den sociala roboten Furhat har visat sig vara relevant för Robot-Assisted Language Learning. I dagsläget har Furhat en konstant talhastighet, men i tidigare studier har det framkommit att den skulle gynnas av att ha en mer dynamisk text-till-talsyntes. I denna kandidatexamensuppsats har en modell skapats, i syfte att klassificera meningar på svenska efter komplexitet. Modellen användes för att avgöra vilka meningar som bör ha en lägre talhastighet, i syfte att öka deras begripligheten för elever som lär sig svenska som andraspråk. För att utvärdera modellen jämfördes komplexitetsbedömningen me

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Федоров, Д. П. "Comparison of classifiers based on the decision tree." Thesis, ХНУРЕ, 2021. https://openarchive.nure.ua/handle/document/16430.

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The main purpose of this work is to compare classifiers. Random Forest and XGBoost are two popular machine learning algorithms. In this paper, we looked at how they work, compared their features, and obtained accurate results from their robots.

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Калайчев, Г. В. "Machine learning in classification tasks." Thesis, ХНУРЕ, 2021. https://openarchive.nure.ua/handle/document/16433.

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The main goal of this work is to show the ways to use machine learning algorithms to solve classification tasks. One of the most efficient algorithms is Gradient Boosting (XGB Classier). This is a method which is usually used in competitions because of his speed and opportunity to work with big amount of data.

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Виноградов, М. О. "Система прогнозування та аналiзу цін інтернет-магазинів". Master's thesis, Сумський державний університет, 2019. http://essuir.sumdu.edu.ua/handle/123456789/76493.

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Розроблено веб-додаток для відображення результатів аналізу тар прогнозування цін інтернет магазинів, створено скрапер, що збирає та аналізує дані про смартфони інтернет магазинів із ресурсу hotline.ua, створено та реалізовано математичну модель для прогнозування цін на товари, розроблено REST API сервер для доступу, до зібраних даних, а також, зручне для користувача, відображення характеристик смартфонів інтернет-магазинів у контексті порівняння їх цін та характеристик зі схожими моделями.

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Villafuerte, Chacnama Frank Fernando. "Análisis comparativo de modelos de pronóstico ARIMA y XGBoost aplicados a las series mensuales de ventas en una empresa certificadora." Bachelor's thesis, Universidad Nacional Mayor de San Marcos, 2021. https://hdl.handle.net/20.500.12672/17331.

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Para una conocida empresa en certificaciones a nivel nacional e internacional, es importante hacer un seguimiento de las ventas de las diferentes unidades de negocio, además, de contar con estimaciones para poder tomar decisiones en cuanto a la ejecución presupuestal, asimismo, permite tener una forma de medir el performance que va teniendo la empresa en ventas. En este trabajo se realiza una comparación del poder predictivo de modelos de series de tiempo aplicados en las ventas históricas de la empresa, utilizando como base teórica el enfoque de dominio de tiempo (Box y Jenkins) con el

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Matos, Sara Madeira. "Interpretable models of loss given default." Master's thesis, Instituto Superior de Economia e Gestão, 2021. http://hdl.handle.net/10400.5/20981.

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Mestrado em Econometria Aplicada e Previsão<br>A gestão do risco de crédito é uma área em que os reguladores esperam que os bancos adotem modelos de risco transparentes e auditáveis colocando de parte o uso de modelos de black-box apesar destes serem mais precisos. Neste estudo, mostramos que os bancos não precisam de sacrificar a precisão preditiva ao custo da transparência do modelo para estar em conformidade com os requisitos regulatórios. Ilustramos isso mostrando que as previsões de perdas de crédito fornecidas por um modelo black-box podem ser facilmente explicadas em termos dos seus inp

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Sainct, Benoît. "Contributions statistiques à l'analyse de mégadonnées publiques." Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30053.

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L'objectif de cette thèse est de proposer un ensemble d'outils méthodologiques pour répondre à deux problématiques : la prédiction de masse salariale des collectivités, et l'analyse de leurs données de fiscalité. Pour la première, les travaux s'articulent à nouveau autour de deux thèmes statistiques : la sélection de modèle de série temporelle, et l'analyse de données fonctionnelles. Du fait de la complexité des données et des fortes contraintes de temps de calcul, un rassemblement de l'information a été privilégié. Nous avons utilisé en particulier l'Analyse en Composantes Principales Fonctio

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Kinnander, Mathias. "Predicting profitability of new customers using gradient boosting tree models : Evaluating the predictive capabilities of the XGBoost, LightGBM and CatBoost algorithms." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-19171.

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In the context of providing credit online to customers in retail shops, the provider must perform risk assessments quickly and often based on scarce historical data. This can be achieved by automating the process with Machine Learning algorithms. Gradient Boosting Tree algorithms have demonstrated to be capable in a wide range of application scenarios. However, they are yet to be implemented for predicting the profitability of new customers based solely on the customers’ first purchases. This study aims to evaluate the predictive performance of the XGBoost, LightGBM, and CatBoost algorithms in

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Shahdi, Arya. "Physics-guided Machine Learning Approaches for Applications in Geothermal Energy Prediction." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103603.

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In the area of geothermal energy mapping, scientists have used physics-based models and bottom-hole temperature measurements from oil and gas wells to generate heat flow and temperature-at-depth maps. Given the uncertainties and simplifying assumptions associated with the current state of physics-based models used in this field, this thesis explores an alternate approach for locating geothermally active regions using machine learning methods coupled with physics knowledge of geothermal energy problems, in the emerging field of physics-guided machine learning. There are two primary contribution

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Bugo, Laura. "authorship analysis: studio delle metodologie e sviluppo di un sistema di riconoscimento." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Lo scopo del lavoro è quello di implementare un programma per il riconoscimento degli autori che permetta di individuare, tra un gruppo di sospetti, l'autore di un testo ignoto, avendo in input alcuni testi per ogni sospetto. Dai testi degli autori sono state estratte delle caratteristiche stilistiche costruite basandosi su esperimenti presenti in letteratura e attraverso l'utilizzo di nuove tecnologie non ancora testate nel problema dell'authorship attribution, Le caratteristiche stilistiche costruite sono quindi utilizzate per riconoscere gli autori dei testi di cui non è nota la paternità

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Gert, Oskar. "Using Machine Learning as a Tool to Improve Train Wheel Overhaul Efficiency." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-171121.

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This thesis develops a method for using machine learning in a industrial pro-cess. The implementation of this machine learning model aimed to reduce costsand increase efficiency of train wheel overhaul in partnership with the AustrianFederal Railroads, Oebb. Different machine learning models as well as categoryencodings were tested to find which performed best on the data set. In addition,differently sized training sets were used to determine whether size of the trainingset affected the results. The implementation shows that Oebb can save moneyand increase efficiency of train wheel overhaul by

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Benedetti, Riccardo. "Neuroimaging e disturbo dello spettro autistico: classificazione con approccio explainable AI." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Il disturbo dello spettro autistico (Autistic Spectrum Disorder - ASD) indica un ventaglio di diagnosi che vanno dalla Sindrome di Asperger all'autismo e che sono accumunate dalla presenza di sintomi comuni, che compromettono l'aspetto comportamentale e i rapporti con la società del soggetto. Al momento la diagnosi di ASD avviene affidandosi a test standardizzati riconosciuti eseguiti da personale medico specializzato. Negli ultimi anni si sono però generati diversi dataset di neuroimaging in cui vengono raccolte le immagini di risonanza magnetica provenienti da centri differenti e acquisite s

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Horemuz, Michal. "Application of Machine Learning to Financial Trading." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231789.

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Machine learning methods have become powerful tools used in multiple industries. They have been successfully applied to problems such as image recognition, speech recognition and machine translation, among others. In this report, we investigated several machine learning methods for forecasting five different bond indexes. We have implemented and analyzed Feedforward Neural Nets, LSTMs, Q-Networks and Gradient Boosted Trees, and compared them to the Buy&Hold strategy. We performed manual feature extraction based on some popular features used in the industry. The features were extracted from

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Fürderer, Niklas. "A Study of an Iterative User-Specific Human Activity Classification Approach." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-253802.

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Applications for sensor-based human activity recognition use the latest algorithms for the detection and classiﬁcation of human everyday activities, both for online and ofﬂine use cases. The insights generated by those algorithms can in a next step be used within a wide broad of applications such as safety, ﬁtness tracking, localization, personalized health advice and improved child and elderly care.In order for an algorithm to be performant, a signiﬁcant amount of annotated data from a speciﬁc target audience is required. However, a satisfying data collection process is cost and labor intensi

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Svensson, William. "CAN STATISTICAL MODELS BEAT BENCHMARK PREDICTIONS BASED ON RANKINGS IN TENNIS?" Thesis, Uppsala universitet, Statistiska institutionen, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447384.

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The aim of this thesis is to beat a benchmark prediction of 64.58 percent based on player rankings on the ATP tour in tennis. That means that the player with the best rank in a tennis match is deemed as the winner. Three statistical model are used, logistic regression, random forest and XGBoost. The data are over a period between the years 2000-2010 and has over 60 000 observations with 49 variables each. After the data was prepared, new variables were created and the difference between the two players in hand taken all three statistical models did outperform the benchmark prediction. All thre

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De, Giorgi Marcello. "Tree ensemble methods for Predictive Maintenance: a case study." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22282/.

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Nel lavoro descritto in questa tesi sono stati creati modelli per la manutenzione predittiva di macchine utensili in ambito industriale; in particolare, i modelli realizzati sono stati addestrati sfruttando degli ensemble tree methods con le finalità di: predire il verificarsi di un guasto in macchina con un anticipo tale da permettere l'organizzazione delle squadre di manutenzione; predire la necessità della sostituzione anticipata dell'utensile utilizzato dalla macchina, per mantenere alti gli standard di qualità. Dopo aver dato uno sfondo al contesto industriale in esame, la tesi illustra i

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Liu, Xiaoyang. "Machine Learning Models in Fullerene/Metallofullerene Chromatography Studies." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93737.

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Machine learning methods are now extensively applied in various scientific research areas to make models. Unlike regular models, machine learning based models use a data-driven approach. Machine learning algorithms can learn knowledge that are hard to be recognized, from available data. The data-driven approaches enhance the role of algorithms and computers and then accelerate the computation using alternative views. In this thesis, we explore the possibility of applying machine learning models in the prediction of chromatographic retention behaviors. Chromatographic separation is a key techni

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Fredén, Daniel, and Hampus Larsson. "Forecasting Daily Supermarkets Sales with Machine Learning." Thesis, KTH, Optimeringslära och systemteori, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-276483.

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Improved sales forecasts for individual products in retail stores can have a positive effect both environmentally and economically. Historically these forecasts have been done through a combination of statistical measurements and experience. However, with the increased computational power available in modern computers, there has been an interest in applying machine learning for this problem. The aim of this thesis was to utilize two years of sales data, yearly calendar events, and weather data to investigate which machine learning method could forecast sales the best. The investigated methods

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Revend, War. "Predicting House Prices on the Countryside using Boosted Decision Trees." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279849.

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This thesis intends to evaluate the feasibility of supervised learning models for predicting house prices on the countryside of South Sweden. It is essential for mortgage lenders to have accurate housing valuation algorithms and the current model offered by Booli is not accurate enough when evaluating residence prices on the countryside. Different types of boosted decision trees were implemented to address this issue and their performances were compared to traditional machine learning methods. These different types of supervised learning models were implemented in order to find the best model

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Malmberg, Olle, and Bobby Zhou. "Using Machine Learning to Detect Customer Acquisition Opportunities and Evaluating the Required Organizational Prerequisites." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263056.

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This paper aims to investigate whether or not it is possible to identify users who are about change provider of service with machine learning. It is believed that the Consumer Decision Journey is a better model than traditional funnel models when it comes to depicting the processes which consumers go through, leading up to a purchase. Analytical and operational Customer Relationship Management are presented as possible fields where such implementations can be useful. Based on previous studies, Random Forest and XGBoost were chosen as algorithms to be further evaluated because of its general hi

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Gregório, Rafael Leite. "Modelo híbrido de avaliação de risco de crédito para corporações brasileiras com base em algoritmos de aprendizado de máquina." Universidade Católica de Brasília, 2018. https://bdtd.ucb.br:8443/jspui/handle/tede/2432.

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Sharma, Vibhor. "Early Stratification of Gestational Diabetes Mellitus (GDM) by building and evaluating machine learning models." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281398.

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Gestational diabetes Mellitus (GDM), a condition involving abnormal levels of glucose in the blood plasma has seen a rapid surge amongst the gestating mothers belonging to different regions and ethnicities around the world. Cur- rent method of screening and diagnosing GDM is restricted to Oral Glucose Tolerance Test (OGTT). With the advent of machine learning algorithms, the healthcare has seen a surge of machine learning methods for disease diag- nosis which are increasingly being employed in a clinical setup. Yet in the area of GDM, there has not been wide spread utilization of these algorit

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Marini, Luca. "Analisi dei costi e benefici di Google Cloud Platform per algoritmi di Machine Learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21602/.

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Al giorno d’oggi sempre più aziende richiedono una maggiore flessibilità da poter applicare nell'esecuzione dei propri task computazionali. In questa tesi si vuole svolgere un'analisi dei costi e benefici che Google Cloud Platform (GCP), uno dei leader tra le suite di servizi di cloud computing, offre per l'esecuzione di algoritmi di machine learning. Vengono presentate diverse metodologie con le quali è possibile eseguire questi algoritmi sulla piattaforma in cloud di Google. In particolare, viene approfondito il tema della conteinerizzazione per creare un framework di automatizzazione che ri

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Wessman, Filip. "Advanced Algorithms for Classification and Anomaly Detection on Log File Data : Comparative study of different Machine Learning Approaches." Thesis, Mittuniversitetet, Institutionen för informationssystem och –teknologi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-43175.

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Background: A problematic area in today’s large scale distributed systems is the exponential amount of growing log data. Finding anomalies by observing and monitoring this data with manual human inspection methods becomes progressively more challenging, complex and time consuming. This is vital for making these systems available around-the-clock. Aim: The main objective of this study is to determine which are the most suitable Machine Learning (ML) algorithms and if they can live up to needs and requirements regarding optimization and efficiency in the log data monitoring area. Including what

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Mikulec, Marek. "Systém zabezpečeného přenosu a zpracování dat z aktigrafu." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413070.

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The new Health 4.0 concept brings the idea of combining modern technologies from field of science and technology with research in healthcare and medicine. This work realizes a system of secured actigraph data transfer and preprocessing based on the concept of Health 4.0. The system is successfully designed, implemented, tested and secured. With the help of a non-invasive method of monitoring the movement and temperature of the subject using the GENEActiv actigraph allows the system to securely transfer, process and evaluate the subject's sleep data using the machine learning algorithm XGBoost.

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Granström, Daria, and Johan Abrahamsson. "Loan Default Prediction using Supervised Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252312.

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It is essential for a bank to estimate the credit risk it carries and the magnitude of exposure it has in case of non-performing customers. Estimation of this kind of risk has been done by statistical methods through decades and with respect to recent development in the field of machine learning, there has been an interest in investigating if machine learning techniques can perform better quantification of the risk. The aim of this thesis is to examine which method from a chosen set of machine learning techniques exhibits the best performance in default prediction with regards to chosen model

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Wigren, Richard, and Filip Cornell. "Marketing Mix Modelling: A comparative study of statistical models." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160082.

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Deciding the optimal media advertisement spending is a complex issue that many companies today are facing. With the rise of new ways to market products, the choices can appear infinite. One methodical way to do this is to use Marketing Mix Modelling (MMM), in which statistical modelling is used to attribute sales to media spendings. However, many problems arise during the modelling. Modelling and mitigation of uncertainty, time-dependencies of sales, incorporation of expert information and interpretation of models are all issues that need to be addressed. This thesis aims to investigate the ef

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Cheltuitor, Alexandru, and Niklas Jones-Quartey. "Predicting Multimodal Rehabilitation Outcomes using Machine Learning." Thesis, Uppsala universitet, Institutionen för informatik och media, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-413568.

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Chronic pain is a complex health issue and a major cause of disability worldwide. Although multimodal rehabilitation (MMR) has been recognized as an effective form of treatment for chronic pain, some patients do not benefit from it. If treatment outcomes could be reliably predicted, then patients who would benefit more from MMR could be prioritized over others. Machine learning has been proven capable of accurately predicting outcomes in other healthcare related domains. Therefore, this study aims to investigate the use of it to predict outcomes of MMR, using data from the Swedish Quality Regi

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Kováč, Daniel. "Diferenční analýza multilingválního řečového korpusu pacientů s neurodegenerativními onemocněními." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413254.

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This diploma thesis focuses on the automated diagnosis of hypokinetic dysarthria in the multilingual speech corpus, which is a motor speech disorder that occurs in patients with neurodegenerative diseases such as Parkinson’s disease. The automatic speech recognition approach to diagnosis is based on the acoustic analysis of speech and subsequent use of mathematical models. The popularity of this method is on the rise due to its objectivity and the possibility of working simultaneously on different languages. The aim of this work is to find out which acoustic parameters have high discriminative

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Pettersson, Gustav, and John Almqvist. "Lavinprognoser och maskininlärning : Att prediktera lavinprognoser med maskininlärning och väderdata." Thesis, Uppsala universitet, Institutionen för informatik och media, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-387205.

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Denna forskningsansats undersöker genomförbarheten i att prediktera lavinfara med hjälp av ma-skininlärning i form avXGBoostoch väderdata. Lavinprognoser och meterologisk vädermodelldata harsamlats in för de sex svenska fjällområden där Naturvårdsveket genomlavinprognoser.sepublicerar lavin-prognoser. Lavinprognoserna har hämtats frånlavinprognoser.seoch den vädermodelldata som användsär hämtad från prognosmodellen MESAN, som produceras och tillhandahålls av Sveriges meteorologiskaoch hydrologiska institut. 40 modeller av typenXGBoosthar sedan tränats på denna datamängd, medsyfte att predikter

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Falk, Anton, and Daniel Holmgren. "Sales Forecasting by Assembly of Multiple Machine Learning Methods : A stacking approach to supervised machine learning." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184317.

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Today, digitalization is a key factor for businesses to enhance growth and gain advantages and insight in their operations. Both in planning operations and understanding customers the digitalization processes today have key roles, and companies are spending more and more resources in this fields to gain critical insights and enhance growth. The fast-food industry is no exception where restaurants need to be highly flexible and agile in their work. With this, there exists an immense demand for knowledge and insights to help restaurants plan their daily operations and there is a great need for o

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Karlsson, Henrik. "Uplift Modeling : Identifying Optimal Treatment Group Allocation and Whom to Contact to Maximize Return on Investment." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157962.

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This report investigates the possibilities to model the causal effect of treatment within the insurance domain to increase return on investment of sales through telemarketing. In order to capture the causal effect, two or more subgroups are required where one group receives control treatment. Two different uplift models model the causal effect of treatment, Class Transformation Method, and Modeling Uplift Directly with Random Forests. Both methods are evaluated by the Qini curve and the Qini coefficient. To model the causal effect of treatment, the comparison with a control group is a necessit

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Oldenkamp, Emiel. "Using supervised learning methods to predict the stop duration of heavy vehicles." Thesis, Mälardalens högskola, Akademin för utbildning, kultur och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-50977.

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In this thesis project, we attempt to predict the stop duration of heavy vehicles using data based on GPS positions collected in a previous project. All of the training and prediction is done in AWS SageMaker, and we explore possibilities with Linear Learner, K-Nearest Neighbors and XGBoost, all of which are explained in this paper. Although we were not able to construct a production-grade model within the time frame of the thesis, we were able to show that the potential for such a model does exist given more time, and propose some suggestions for the paths one can take to improve on the endpo

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Fernandez, Sanchez Javier. "Knowledge Discovery and Data Mining Using Demographic and Clinical Data to Diagnose Heart Disease." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233978.

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Cardiovascular disease (CVD) is the leading cause of morbidity, mortality, premature death and reduced quality of life for the citizens of the EU. It has been reported that CVD represents a major economic load on health care sys- tems in terms of hospitalizations, rehabilitation services, physician visits and medication. Data Mining techniques with clinical data has become an interesting tool to prevent, diagnose or treat CVD. In this thesis, Knowledge Dis- covery and Data Mining (KDD) was employed to analyse clinical and demographic data, which could be used to diagnose coronary artery diseas

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Zoghi, Zeinab. "Ensemble Classifier Design and Performance Evaluation for Intrusion Detection Using UNSW-NB15 Dataset." University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596756673292254.

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D'AMATO, VINCENZO STEFANO. "Deep Multi Temporal Scale Networks for Human Motion Analysis." Doctoral thesis, Università degli studi di Genova, 2023. https://hdl.handle.net/11567/1104759.

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The movement of human beings appears to respond to a complex motor system that contains signals at different hierarchical levels. For example, an action such as ``grasping a glass on a table'' represents a high-level action, but to perform this task, the body needs several motor inputs that include the activation of different joints of the body (shoulder, arm, hand, fingers, etc.). Each of these different joints/muscles have a different size, responsiveness, and precision with a complex non-linearly stratified temporal dimension where every muscle has its temporal scale. Parts such as the

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LIU, SHUN-FU, and 劉順富. "Forecasting Taiwan Index Futures with XGBOOST Algorithm." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/m782y5.

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碩士<br>國立雲林科技大學<br>財務金融系<br>107<br>Over the past two decades, a growing large body of investors has traded the Taiwan index weighted futures (TX) for arbitrage or hedging purpose. As the information released by the Taiwan Futures Exchange is more diversified, the related researches on TX is also deeper. In recent years, with the rise of artificial intelligence and big data, many peopleuse computer learning and data mining to construct trading algorithms in order to predict stock price. This study aims to explore the predictability of TX price by using available post-market data, such as opening

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Chiu, Chien-Ju, and 邱建儒. "PM2.5 Forecasting based on Attention Neural Network and XGBoost." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/7a9e3f.

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碩士<br>國立交通大學<br>資訊管理研究所<br>106<br>With air pollution becoming a global concern, scientists are committed to the study of air pollution. In the field of air pollution prediction, there have been good results in experimental research so far, but few studies have taken the weather forecast information and the properties of air pollution drifting. In our study, we proposed a wind-sensitive attention neural network model to learn the influence of wind direction and wind speed on the changes of spatial-temporal PM2.5 concentrations in neighboring areas. Then, preliminary predictions for PM2.5 are ma

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Liu, Hsin-Yu, and 劉欣諭. "Constructing the Conservative Equity Portfolio by the XGBoost Model." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/6dx4h9.

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碩士<br>國立中山大學<br>財務管理學系研究所<br>107<br>This study uses the data of Taiwan-listed companies from 1997 to 2018 and applying the conservative investment formula proposed by Blitz and Vliet (2018) in the Taiwan market, using three simple factors, low volatility, high dividends and positive momentum to form a portfolio. Then use the machine learning algorithm (XGBoost Model) proposed by Chen and Guestrin (2016), and use the above three factors, but consider the different calculation periods to build a return model. In order to test the effectiveness of the model, we use the original conservative portf

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Ding-KaiTseng and 曾鼎凱. "A NetFlow Based Malicious Traffic Detection Research using XGBoost." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/n46zxx.

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Pao, Chi-Hsien, and 鮑奇賢. "XGBIR: An XGBoost-based IR Drop Predictor for Early-stage Power Delivery Network." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/9e55ap.

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碩士<br>國立交通大學<br>電信工程研究所<br>108<br>在晶片的電源輸送網路裡，當從封裝外提供電源到晶片內部時會產生電壓衰退的現象，而間接造成晶片工作不正常，因此我們必須對一個電源輸送網路做分析來確保提供給晶片內部的電壓都能夠維持一定的範圍。此篇提出了以極限梯度上升為基礎的方式預測電壓衰退，並提出了幾種參數萃取的方式能夠更加的表現出電源輸送網路的行為，我們的預測器能夠預測任意的有規則結構的電源輸送網路上的任一個節點的電壓降，將能更有效率並且更快速的預測出有潛在違反電壓限制的區域，讓電源輸送網路的設計者能夠透過我們的預測器來最佳化網路的設計。

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Herrmann, Vojtěch. "Moderní predikční metody pro finanční časové řady." Master's thesis, 2021. http://www.nusl.cz/ntk/nusl-437908.

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This thesis deals with comparing two approaches to modelling and predicting time series: a traditional one (the ARIMAX model) and a modern one (gradiently boosted decision trees within the framework of the XGBoost library). In the first part of the thesis we introduce the theoretical framework of supervised learning, the ARIMAX model and gradient boosting in the context of decision trees. In the second part we fit the ARIMAX and XGBoost models which both predict a specific time series, the daily volume of the S&P 500 index, which is a crucial task in many branches. After that we compare the re

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Yeh, Jih-Yang, and 葉日揚. "A Phishing Website Detection Service Mechanism Utilizing XGBoost Classification Model and Key-term Extraction Method." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/pan8hn.

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碩士<br>國立臺灣科技大學<br>資訊工程系<br>107<br>This research proposes a phishing website detection mechanism that combines an XGBoost based phishing website classifier and the key-term extraction method. Some pre-processing techniques are also developed to enhance the performance. XGBoost is well known for its high efficiency and accuracy, and the key-term based detection method helps to minimize the false positive rate of the phishing website classification model. The key-term extraction method is based on two observation: Phishers usually try to make phishing websites look similar to their imitation ta

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