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Semi-supervised time series classification
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006The problem of time series classification has attracted great interest in the last decade. However current research assumes the existence of large amounts of labeled training data. In reality, such data may be very difficult or expensive to obtain. For example, it may require the time and expertise of cardiologists, space launch technicians, or other ...
Li Wei, Eamonn Keogh
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Time Series based Gastropod Classification
2018 10th International Conference on Knowledge and Smart Technology (KST), 2018This paper presents Series-K, an automatic Gastropod classification system based on Time Series and k-Nearest Neighbor. Species evolve over time. Biologists have discovered, attempted to describe and put them into categories. Our proposed method automatically processes gastropods’ images to help the malacologists accurately identify gastropods into ...
Janya Onpans +2 more
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Time series envelopes for classification
2010 5th IEEE International Conference Intelligent Systems, 2010In this paper we considered a streaming data classification problem. First we introduced a concept of upper and lower envelopes of time series in order to reduce dimensionality of them. Next we merged machine learning tools like feedforward neural networks for selection principal attributes as well as decision rules of the form if … then … for time ...
Maciej Krawczak, Grazyna Szkatula
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Cost Sensitive Time-Series Classification
2017This paper investigates the problem of highly imbalanced time-series classification using shapelets, short patterns that best characterize the target time-series, which are highly discriminative. The current state-of-the-art approach learns generalized shapelets along with weights of the classification hyperplane via a classical cost-insensitive loss ...
Shoumik Roychoudhury +2 more
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Exploratory Classification of Time-Series
2021In this paper, an exploratory hierarchical method to classify variables is introduced as an alternative to principal component analysis when dealing with stock-exchange price time-series. The method is based on a particular principal component analysis applied to pairs of variables, each one associated to a group to be merged.
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Classification of short time series
2009Many time series are of short duration because data acquisition has, of necessity, proceeded for but a brief term. Such data have previously often been analyzed by methods that either do not explicitly take into account time related changes or that are designed for long time series.
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Model-Based Time Series Classification
2014We propose MTSC, a filter-and-refine framework for time series Nearest Neighbor (NN) classification. Training time series belonging to certain classes are first modeled through Hidden Markov Models (HMMs). Given an unlabeled query, and at the filter step, we identify the top K models that have most likely produced the query.
Alexios Kotsifakos +1 more
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Time Series Classification at Scale
2019This thesis develops scalable algorithms and techniques to classify large amount of time series data. Nowadays, many real-world applications are generating huge amount of time series data. This wealth of data is required to create finer and more accurate classification models that allow us to learn from the data.
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Classification methods for time series
2017The focus of this thesis is on the classification methods of time series, including clustering and discriminating techniques. The study in this thesis involves the examination of a number of existing approaches to time series classification, as well as the proposal of some new methodologies.
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