2019
DOI: 10.1109/jas.2019.1911747
|View full text |Cite
|
Sign up to set email alerts
|

The UCR time series archive

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
477
0

Year Published

2019
2019
2024
2024

Publication Types

Select...
4
3
2

Relationship

2
7

Authors

Journals

citations
Cited by 771 publications
(477 citation statements)
references
References 36 publications
0
477
0
Order By: Relevance
“…The vast majority of papers on time series classification consider some improvements on some of the UCR datasets as a necessary and sufficient condition for publication [1] [16]. However, as [22] recently noted, many of the datasets in the archive were created by using a shape-based measure to extract exemplars from a longer time series. Thus, it is tautological to find that shapebased measures work well on this ubiquitous benchmark.…”
Section: 1mentioning
confidence: 99%
“…The vast majority of papers on time series classification consider some improvements on some of the UCR datasets as a necessary and sufficient condition for publication [1] [16]. However, as [22] recently noted, many of the datasets in the archive were created by using a shape-based measure to extract exemplars from a longer time series. Thus, it is tautological to find that shapebased measures work well on this ubiquitous benchmark.…”
Section: 1mentioning
confidence: 99%
“…The dataset contains 10,934 train cases and is sampled at 2kHz for a series length of 4000. This exceeds the largest train set from ElectricDevices of 8926 and series length from HouseTwenty of 3000 in the 128 UCR datasets [7] (we will donate this data to the archive for the next release). A combination of these factors causes a problem for many standard approaches which would find it difficult to build a model on this data with reasonable time and memory.…”
Section: Whale Acoustics Use Casementioning
confidence: 99%
“…The UCR TSC repository has recently been expanded [7] from 85 to 128 datasets available and including more complex problems such as series of differing length and series with missing values. We make use of this expanded repository in our experiments but exclude datasets with missing values and variable length series due to the currently inability of included classifiers to handle such data.…”
Section: Introductionmentioning
confidence: 99%
“…The archive got an update in 2018, expanding it from 85 to 128 datasets. The new datasets on average have a higher number of training samples and also contain sets with variable length time series to represent many real-world problems [6], ranging from the movement of insect wings to the energy consumption profile of electronic devices. Most datasets in the archive have already been Z-score normalized.…”
Section: Datamentioning
confidence: 99%