Essays about: "analys av tidsserier"
Showing result 1 - 5 of 36 essays containing the words analys av tidsserier.
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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)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
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2. Portfolio Risk Modelling in Venture Debt
University essay from KTH/Matematisk statistikAbstract : 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
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3. Optimizing Resource Allocation in Kubernetes : A Hybrid Auto-Scaling Approach
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)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
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4. Clustering of Unevenly Spaced Mixed Data Time Series
University essay from KTH/Matematisk statistikAbstract : 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
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5. LSTM-based Directional Stock Price Forecasting for Intraday Quantitative Trading
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)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