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Visar resultat 1 - 5 av 34 uppsatser som matchar ovanstående sökkriterier.

  1. 1. Portfolio Risk Modelling in Venture Debt

    Master-uppsats, KTH/Matematisk statistik

    Författare :John Eriksson; Jacob Holmberg; [2023]
    Nyckelord :Startup Default Probability; Venture Debt; Gaussian Copula; Value-at-Risk; Expected Shortfall; Exposure at Default; Loss Given Default; Forecast; Linear Dynamic System; ARIMA Time Series; Monte Carlo Simulation; Linear Regression; Central Limit Theorem;

    Sammanfattning : This thesis project is an experimental study on how to approach quantitative portfolio credit risk modelling in Venture Debt portfolios. Facing a lack of applicable default data from ArK and publicly available sets, as well as seeking to capture companies that fail to service debt obligations before defaulting per se, we present an approach to risk modeling based on trends in revenue. LÄS MER

  3. 2. Data Trustworthiness Assessment for Traffic Condition Participatory Sensing Scenario

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Hairuo Gao; [2022]
    Nyckelord :Participatory sensing; Data trustworthiness assessment; Anomaly detection; Traffic prediction; Deep neural network; Deltagande avkänning; Bedömning av uppgifternas tillförlitlighet; Upptäckt av anomalier; Trafikprognoser; Djupt neuralt nätverk;

    Sammanfattning : Participatory Sensing (PS) is a common mode of data collection where valuable data is gathered from many contributors, each providing data from the user’s or the device’s surroundings via a mobile device, such as a smartphone. This has the advantage of cost-efficiency and wide-scale data collection. LÄS MER

  4. 3. On Modelling Ancillary Services Markets: A Time Series Approach

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Erik Murray; [2022]
    Nyckelord :ARIMA; SARIMA; GARCH; load balancing; ancillary services; electrical grids; FCR-D; ARIMA; SARIMA; GARCH; lastbalans; stödtjänster; kraftnät; FCR-D 2;

    Sammanfattning : So-called ancillary services (AS) have always been critically important for the functioning of an electrical grid, and are becoming even more so with the advent of renewable energy sources. Ancillary services are traded on open markets, and trading on these markets is arguably even more difficult to model than on traditional markets. LÄS MER

  5. 4. Predicting Drought Hazard In Sweden Using Google Earth Engine And Machine Learning Approach

    Master-uppsats, KTH/Hållbar utveckling, miljövetenskap och teknik

    Författare :Jung-ching Kan; [2022]
    Nyckelord :drought forecast; google earth engine; machine learning; Sweden; torkaprognos; google earth engine; maskininlärning; Sverige;

    Sammanfattning : Drought, being one the most complex natural hazards, has a significant impact on society. To mitigate the impact and risk, it is crucial to be able to forecast drought, which is a challenging task. Nowadays, with technology innovations, large amounts of remote sensing data is available on the cloud. LÄS MER

  7. 5. Machine learning embedded automation in financial forecasting : A quantitative case study at Ericsson

    Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

    Författare :Isak Hassbring; [2022]
    Nyckelord :;

    Sammanfattning : In today’s increasingly data-driven world, time series forecasting is becoming a prevalent practice. Business executives can make better decisions aided by insights from financial forecasts. LÄS MER