Sökning: "analys av tidsserier"
Visar resultat 1 - 5 av 46 uppsatser innehållade orden analys av tidsserier.
1. Evaluating Brain-Inspired Machine Learning Models for Time Series Forecasting: A Comparative Study on Dynamical Memory in Reservoir Computing and Neural Networks
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : 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. LÄS MER
2. Portfolio Risk Modelling in Venture Debt
Master-uppsats, KTH/Matematisk statistikSammanfattning : 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. Optimizing Resource Allocation in Kubernetes : A Hybrid Auto-Scaling Approach
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : 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. LÄS MER
4. Clustering of Unevenly Spaced Mixed Data Time Series
Master-uppsats, KTH/Matematisk statistikSammanfattning : 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. LÄS MER
5. LSTM-based Directional Stock Price Forecasting for Intraday Quantitative Trading
Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : 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. LÄS MER