Results 261 to 270 of about 655,816 (301)

A symbolic representation of time series

Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, 2005., 2006
Various representations have been proposed for time series to facilitate similarity searches and discovery of interesting patterns. Although the Euclidean distance and its variants have been most frequently used as similarity measures, they are relatively sensitive to noise and fail to provide meaningful information in many cases.
Qiang Wang 0010, Vasileios Megalooikonomou, Guo Li   +2 more
openaire   +1 more source

CARLA: Self-supervised contrastive representation learning for time series anomaly detection

Pattern Recognition
One main challenge in time series anomaly detection (TSAD) is the lack of labelled data in many real-life scenarios. Most of the existing anomaly detection methods focus on learning the normal behaviour of unlabelled time series in an unsupervised manner.
Zahra Zamanzadeh Darban, Geoffrey I Webb, Shirui Pan   +2 more
exaly   +3 more sources

On time series representations for multi-label NILM

Neural Computing and Applications, 2020
Given only the main power consumption of a household, a non-intrusive load monitoring (NILM) system identifies which appliances are operating. With the rise of Internet of things, running energy disaggregation models on the edge is more and more essential for privacy concerns and economic reasons.
Christoforos Nalmpantis, Dimitris Vrakas
openaire   +1 more source

A new symbolic representation method for time series

Information Sciences, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yucheng Li, Derong Shen
openaire   +1 more source

Hierarchical Multiresolution Representation of Streaming Time Series

Big Data Research, 2021
Abstract Real-time monitoring, analysis and operations in large industrial systems require an accurate but compact data model created on the basis of a large number of data sources continuously generating massive amounts of data modeled as streaming time series. This paper proposes a generic time series representation approach for reducing data model
Igor Manojlovic, Goran Svenda, Aleksandar Erdeljan, Milan Gavric, Darko Capko   +4 more
openaire   +1 more source

Pattern frequency representation for time series classification

2016 IEEE 8th International Conference on Intelligent Systems (IS), 2016
The paper presents a new method for data transformation. The obtained data format enhances the efficiency of the time series classification. The transformation is realized in four steps, namely: time series segmentation, segment's feature representation, segments' binning and pattern frequency representation.
Sergey Milanov, Olga Georgieva
openaire   +1 more source

Learning Representations for Incomplete Time Series Clustering

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
Time-series clustering is an essential unsupervised technique for data analysis, applied to many real-world fields, such as medical analysis and DNA microarray. Existing clustering methods are usually based on the assumption that the data is complete. However, time series in real-world applications often contain missing values.
Qianli Ma 0001, Chuxin Chen, Sen Li 0001, Garrison W. Cottrell   +3 more
openaire   +1 more source

Time Series Classification with Representation Ensembles

2015
Time series has attracted much attention in recent years, with thousands of methods for diverse tasks such as classification, clustering, prediction, and anomaly detection. Among all these tasks, classification is likely the most prominent task, accounting for most of the applications and attention from the research community.
Rafael Giusti, Diego Furtado Silva, Gustavo E. A. P. A. Batista   +2 more
openaire   +1 more source

Time Series Representations

1986
Time series data are usually collected on a monthly, quarterly or annual basis. Such time series provide important economic and demographic information and are published by various institutions on a regular basis.
S. H. C. du Toit, A. G. W. Steyn, R. H. Stumpf   +2 more
openaire   +1 more source

Nominal time series representation for the clustering problem

2012 6th IEEE INTERNATIONAL CONFERENCE INTELLIGENT SYSTEMS, 2012
In this paper we considered time series dimension reduction for clustering problem. The techniques of reduction of dimension of time series is based on the concept of envelopes, aggregation of the envelopes and extracting essential attributes. Essential attributes were nominalized.
Maciej Krawczak, Grazyna Szkatula
openaire   +1 more source