Results 281 to 290 of about 3,979,328 (298)

Advancing Time Series Classification with Multimodal Language Modeling

arXiv.org
For the advancements of time series classification, scrutinizing previous studies, most existing methods adopt a common learning-to-classify paradigm - a time series classifier model tries to learn the relation between sequence inputs and target label ...
Mingyue Cheng, Yiheng Chen, Qi Liu, Zhiding Liu, Yucong Luo   +4 more
semanticscholar   +1 more source

Time Series based Gastropod Classification

2018 10th International Conference on Knowledge and Smart Technology (KST), 2018
This 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, Nutthanon Leelathakul, Sunisa Rimcharoen   +2 more
openaire   +1 more source

Time series envelopes for classification

2010 5th IEEE International Conference Intelligent Systems, 2010
In 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
openaire   +1 more source

Cost Sensitive Time-Series Classification

2017
This 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, Mohamed Ghalwash, Zoran Obradovic   +2 more
openaire   +1 more source

Classification trees for time series

Pattern Recognition, 2012
This paper proposes an extension of classification trees to time series input variables. A new split criterion based on time series proximities is introduced. First, the criterion relies on an adaptive (i.e., parameterized) time series metric to cover both behaviors and values proximities.
Douzal-Chouakria, Ahlame, Amblard, Cécile   +1 more
openaire   +2 more sources

Exploratory Classification of Time-Series

2021
In 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.
openaire   +1 more source

Model-Based Time Series Classification

2014
We 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, Panagiotis Papapetrou   +1 more
openaire   +1 more source

Classification of short time series

2009
Many 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.
openaire   +2 more sources

SelfMatch: Robust semisupervised time‐series classification with self‐distillation

International Journal of Intelligent Systems, 2022
Huanlai Xing, Zhiwen Xiao, Dawei Zhan, Shouxi Luo, Penglin Dai, Ke Li   +5 more
semanticscholar   +1 more source

Time Series Classification at Scale

2019
This 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.
openaire   +1 more source