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Showing result 1 - 5 of 36 essays matching the above criteria.

1. 1. Evaluating Brain-Inspired Machine Learning Models for Time Series Forecasting: A Comparative Study on Dynamical Memory in Reservoir Computing and Neural Networks

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Eddie Nevander Hellström; Johan Slettengren; [2023]
   Keywords : ;

   Abstract : Brain-inspired computing is a promising research field, with potential to encouragebreakthroughs within machine learning and enable us to solve complex problems in a moreefficient way. This study aims to compare the performance of brain-like machine learningalgorithms for time series forecasting. READ MORE

3. 2. Portfolio Risk Modelling in Venture Debt

   University essay from KTH/Matematisk statistik

   Author : John Eriksson; Jacob Holmberg; [2023]
   Keywords : 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;

   Abstract : 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. READ MORE

4. 3. Optimizing Resource Allocation in Kubernetes : A Hybrid Auto-Scaling Approach

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Brando Chiminelli; [2023]
   Keywords : Cloud computing; Microservices; Kubernetes; Container Orchestration; Auto-Scaling; Horizontal Pod Autoscaler HPA ; WorkloadPrediction; Time-Series Forecasting; Molntjänster; Mikrotjänster; Kubernetes; Containerorkestrering; Automatisk Skalning; Horizontal Pod Autoscaler HPA ; Förutsägelse avArbetsbelastning; Prognoser för Tidsserier;

   Abstract : This thesis focuses on addressing the challenges of resource management in cloud environments, specifically in the context of running resource-optimized applications on Kubernetes. The scale and growth of cloud services, coupled with the dynamic nature of workloads, make it difficult to efficiently manage resources and control costs. READ MORE

5. 4. Clustering of Unevenly Spaced Mixed Data Time Series

   University essay from KTH/Matematisk statistik

   Author : Pierre Sinander; Asik Ahmed; [2023]
   Keywords : mixed data time series; unevenly spaced time series; clustering; dynamic time warping; Gower dissimilarity; time warping regularisation; numeriska och kategoriska tidsserier; ojämnt fördelade tidsserier; kluster analys; dynamic time warping; Gower dissimilaritet; regularisering av tidsförvränging;

   Abstract : This thesis explores the feasibility of clustering mixed data and unevenly spaced time series for customer segmentation. The proposed method implements the Gower dissimilarity as the local distance function in dynamic time warping to calculate dissimilarities between mixed data time series. READ MORE

6. 5. LSTM-based Directional Stock Price Forecasting for Intraday Quantitative Trading

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Isabella Mustén Ross; [2023]
   Keywords : Deep Learning; Long-Short-Term-Memory LSTM ; ARIMA; Financial Time Series Forecasting; Algorithmic Trading; Intraday Trading; Stock Prediction; Djupinlärning; LSTM; ARIMA; finansiella tidsserier; algoritmisk aktiehandel; intradagshandel; aktieprediktion;

   Abstract : Deep learning techniques have exhibited remarkable capabilities in capturing nonlinear patterns and dependencies in time series data. Therefore, this study investigates the application of the Long-Short-Term-Memory (LSTM) algorithm for stock price prediction in intraday quantitative trading using Swedish stocks in the OMXS30 index from February 28, 2013, to March 1, 2023. READ MORE