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Compound method of time series classification
Nonlinear Analysis, 2019 Many real phenomenona preserves the properties of chaotic dynamics. However, unambiguous determination of belonging to a group of chaotic systems is difficult and complex problem.
Łukasz Korus, Michał Piórek
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A shapelet transform for time series classification [PDF]
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, 2012 The problem of time series classification (TSC), where we consider any real-valued ordered data a time series, presents a specific machine learning challenge as the ordering of variables is often crucial in finding the best discriminating features. One of the most promising recent approaches is to find shapelets within a data set.
Jason Lines +3 more
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Polarization of forecast densities : a new approach to time series classification [PDF]
, 2014 Time series classification has been extensively explored in many fields of study. Most methods are based on the historical or current information extracted from data.
Inder, Brett +2 more
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Transformer Feature Fusion Network for Time Series Classification [PDF]
Jisuanji kexue, 2023 Model ensemble methods train multiple basic models and use a certain rule to aggregate the output of the basic models for time series classification.However,they mainly focus on two aspects.The first one is which model is chose as the basic mo-del.And ...
DUAN Mengmeng, JIN Cheng
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Time Series Classification Based on Multi-Dimensional Feature Fusion
IEEE Access, 2023 Time series classification is a key problem in data mining, most of existing classification methods directly extract one-dimensional data from one-dimensional features, which cannot effectively express the inter-relation between different time points ...
Shuo Quan +4 more
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Discriminative Dictionary Learning for Time Series Classification
IEEE Access, 2020 Time series symbolization based on the Symbolic Fourier Approximation (SFA) and a sliding window mechanism can effectively improve classification performance. Hence, it has become a research hotspot of time series representation learning.
Wei Zhang +3 more
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Deep Multiple Metric Learning for Time Series Classification
IEEE Access, 2021 Effective distance metric plays an important role in time series classification. Metric learning, which aims to learn a data-adaptive distance metric to measure the distance among samples, has achieved promising results on time series classification ...
Zhi Chen +6 more
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Transfer learning for time series classification [PDF]
2018 IEEE International Conference on Big Data (Big Data), 2018 Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset. This idea has been shown to improve deep neural network's generalization capabilities in many computer vision tasks such
Hassan Ismail Fawaz +4 more
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Hierarchical Factor Classification of Dendrochronological Time-Series
Annals of Silvicultural Research, 2020 In this paper, Hierarchical Factor Classification (HFC), an exploratory method of classification of characters is introduced, in comparison with Principal Component Analysis (PCA) in order to show its advantages, in particular when dealing with time ...
Sergio Camiz +3 more
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On the Suitability of MODIS Time Series Metrics to Map Vegetation Types in Dry Savanna Ecosystems: A Case Study in the Kalahari of NE Namibia [PDF]
, 2009 The characterization and evaluation of the recent status of biodiversity in Southern Africa’s Savannas is a major prerequisite for suitable and sustainable land management and conservation purposes.
Martin Herold +19 more
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