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.
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Showing 1 to 20 of 23 for “"Time Series Classification"”.
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Time Series classification through transformation and ensembles
The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, offers a specific challenge. Unlike traditional classification problems, the ordering of attributes is often crucial for identifying discriminatory features between classes. TSC problems …
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Sparse Representation and its Application to Multivariate Time Series Classification
… to be sparse within a domain, such as spatial, time, or timefrequency domain, and this sparse characteristics of such signals can be obtained through the SR. The ECG signal, for instance, is typically a temporal sparse signal, comprises of various periodic activities such as time delay and …
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Sparse Representation and its Application to Multivariate Time Series Classification
… to be sparse within a domain, such as spatial, time, or timefrequency domain, and this sparse characteristics of such signals can be obtained through the SR. The ECG signal, for instance, is typically a temporal sparse signal, comprises of various periodic activities such as time delay and …
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Grounding Time Series in Language: Interpretable Reasoning with Large Language Models
Can large language models (LLMs) classify time-series data by reasoning like a domain expert—if given the right language? We propose a method that expresses statistical time-series features in natural language, enabling LLMs to perform classification with structured, interpretable reasoning. By …
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Mining time-series data using discriminative subsequences
Time-series data is abundant, and must be analysed to extract usable knowledge. Local-shape-based methods offer improved performance for many problems, and a comprehensible method of understanding both data and models. For time-series classification, we transform the data into a local-shape space …
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Latent source models for nonparametric inference
… methods in the three specific case studies of time series classification, online collaborative filtering, and patch-based image segmentation. To do so, for each of these problems, we prescribe a probabilistic model in which the data appear generated from unknown "latent sources" that capture …
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On the classification of time series and cross wavelet phase variance
… to explore underlying characteristic features of time series data. Its application in large time series classification experiments, however, has been severely limited due to the large amount of redundant associated information. By extending the capabilities of the CWT to perform cross wavelet …
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Adjusting for Autocorrelated Errors in Neural Networks for Time Series
Time series are everywhere and exist in a wide range of domains. Electrical activities of manufacturing equipment, electrocardiograms, traffic occupancy rates, currency exchange rates, speech signals, and atmospheric measurements can all be seen as examples of time series. Modeling time series …
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Automatic feature extraction for time series analysis using deep and machine learning
Time series analysis is crucial in understanding and extracting valuable insights from temporal data, capturing the inherent patterns, trends, and dependencies that evolve over time. This study concisely overviews the state of the art key components and methodologies involved in time series …
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Trend or no trend : a novel nonparametric method for classifying time series
In supervised classification, one attempts to learn a model of how objects map to labels by selecting the best model from some model space. The choice of model space encodes assumptions about the problem. We propose a setting for model specification and selection in supervised learning based on a …
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Real-time process monitoring of Spark-Assisted Chemical Engraving (SACE) machine with data-driven techniques
… for process control by developing a real-time SACE process monitoring methodology for anomaly detection in glass microchannel fabrication. The first step in the methodology introduces a characterization algorithm that segments the machining current into formation, discharging, and silent …
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Applications of Granger Causality to Magnetoencephalography Research, Short Trial Time Series Analysis, and the Study of Decision Making
Causality analysis is an approach to time series analysis that is being used increasingly to investigate neuroimaging data. The reason for its popularity is the useful perspective it provides in describing the ordered operations of various brain regions using indirectly and passively measured …
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Learning to prevent healthcare-associated infections : leveraging data across time and space to improve local predictions
… the inherent evolution of that risk over time, and the relative rarity of adverse outcomes, institutional differences and the lack of ground truth. In this thesis we tackle these challenges in the context of predicting healthcare-associated infections (HAIs). HAIs are a serious problem in …
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Algorithm Hardware Codesign for High Performance Neuromorphic Computing
… we apply proposed algorithm to multivariate time series classification tasks to demonstrate its advantages. On hardware level, we develop a systematic solution on FPGA that is optimized for proposed SNN model to enable high performance inference. In addition, we also explore emerging devices, …
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A Bayesian latent time-series model for switching temporal interaction analysis
We introduce a Bayesian discrete-time framework for switching-interaction analysis under uncertainty, in which latent interactions, switching pattern and signal states and dynamics are inferred from noisy and possibly missing observations of these signals. We propose reasoning over posterior …
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Surface electromyography signal classification using SFDN+DNN for hand gesture recognition
… The classifier used in this thesis is a Time Wrapped CNN+LSTM. The contribution of this research is to predict the hand movements (hand gestures) with a very high accuracy in order to enhance the efficiency of mechanical prosthetic hands and mimic the natural hand movement. To ensure the …
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Relational Outlier Detection: Techniques and Applications
… data, and (3) outlier detection in categorized time series data. For the first task, existing solutions for mixed-type data mostly focus on computational efficiency, and their strategies are mostly heuristic driven, lacking a statistical foundation. The proposed contributions of our work …
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OPTIMAL SUBSEQUENCE BIJECTION AND CLASSIFICATION OF IMBALANCED DATA SETS
Time series are common in many research fields. Since both a query and a target sequence may be noisy, i.e., contain some outlier elements, it is desirable to exclude the outlier elements from matching in order to obtain a robust matching performance. Moreover, in many applications like shape …
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Toward Robust and Generalizable Spatiotemporal Modeling for Tasks beyond Forecasting and Classification
… the brittleness of conventional forecasting and classification pipelines. This dissertation advances spatiotemporal modeling by addressing three critical challenges that frequently arise in real-world applications but fall outside the scope of traditional forecasting and classification: anomaly …
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