Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 15 of 15 for “"time-series clustering"”.
-
Multivariate time series clustering using kernel variant multi-way principal component analysis
Clustering multivariate time series data has been a challenging task for researchers since data has multiple dimensions to consider such as auto-correlations and cross-correlations whereas multivariate time series data has been prevailing in diverse areas for decades. However, for a short-period …
-
Prédiction de la tendance des actions basée sur les réseaux convolutifs graphiques et les LSTM
… to predicting the trend of stocks in which a clustering model is employed to mine the stock trends patterns from historical stock price data. Stock series clustering is a special kind of time series clustering. We aim to find out the trend types, e.g. rising, falling and others, of a stock at …
-
Analysis of Respiratory Time Series Data for Breathing Discomfort Detection Prior to Sleep Onset During APAP Therapy
… of their therapy sessions. Specifically, time-series clustering is performed on sequential respiratory data to identify groups of patients with similar breathing patterns. The independence between clusters and variables pertaining to patients’ demographic characteristics, therapy settings, …
-
Building Energy Profile Clustering Based on Energy Consumption Patterns
… and magnitude. In this thesis, we introduce a clustering technique that is capable of preserving both temporal patterns and total consumption of load shapes from customers’ energy data. The proposed approach first overpopulates clusters as the initial stage to preserve the accuracy and merges …
-
Autoencoder-based multivariate time series anomaly detection and clustering for diagnosis of mechatronic systems
… also called representation, of the Autoencoder. Time windows with different lengths from multivariate time-series data as its input are used during training and inferencing of the Artificial Neural Network (ANN) for analyzing the fault detection performances’ time dependence with transient data. …
-
Understanding Urban Vibrancy and Third Places: A Computational Study of the Social Life of Cities Through Multi-Source Digital Trace Data
… and street-level imagery,the thesis develops a series of complementary empirical studies examining gendered activity patterns, national-scale behavioural structures, and the visual and perceptual characteristics of urban environments. Spatial econometric models, multivariate time series …
-
Globalization for Scalable Short-term Forecasting of Heterogeneous Loads
… with diverse characteristics while ensuring timely and accurate predictions. While intuitive and locally accurate, traditional local forecasting models (LFMs) struggle with scalability, becoming computationally expensive and less efficient as network size and data volume grow. In contrast, …
-
A Framework for Generalizing Uncertainty in Mobile Network Traffic Prediction
… crucial role in the efficient operation of real-time and near-real-time network management. The contributions of this thesis are twofold. The first introduces a novel cluster-train-predict framework that leverages domain knowledge to identify unique timeseries sub-behaviors within aggregates of …
-
Structure combination of forecasting models with application in the energy sector
… smoothing models using synthetic data and real time series, from the electricity sector. It starts with a literature review on combining forecasts and ensembles of neural networks, and highlights their use in forecasting within the energy sector. Research gaps are identified and the questions to …
-
Enhancing health equity in antidiabetic medication use and diabetes care outcomes through data science approaches
… 1.072]). In the second study, we applied a novel time-series clustering approach to characterize long-term antidiabetic medication use patterns over a three-year period. We defined five different transitions (switch, intensification, de-intensification, discontinuation, and re-initiation) based on …
-
Scalable Autonomous Network Control using Minimally Informed Agents
… a limited region per asset can reduce response times and network pressure. The accuracy of limited measures for estimating criticality in dynamic simulations is studied, modelling criticality like an infectious disease. Accuracy benchmarking is theoretically derived. Information bounded …
-
Estudio estadístico de la calidad de las aguas en la cuenca hidrográfica del río Ebro
… de Kohonen, la descomposición de funciones en Series Wavelet o el algoritmo MMCT. Este último, creado por el autor de la Tesis, representa un nuevo método de agrupación de series temporales multivariantes inspirado en el algoritmo de Singhal-Seborg, y consiste en la obtención de una matriz de …
-
Setting-up the decarbonisation of islands: models and technologies for the energy transition
L'abstract è presente nell'allegato / the abstract is in the attachment
-
Identification of glucocorticoid-regulated genes and inferring their network focused on the glucocorticoid receptor in childhood leukaemia, based on microarray data and pathway databases
… and sharing networks and pathways. Four emergent clustering methods are used: Self organising maps (SOM), Emergent self organising maps (ESOM), the Short Time series Expression Miner (STEM) and Fuzzy clustering by Local Approximation of MEmbership (FLAME). These genes are used in the following …