2020
DOI: 10.1007/s10618-020-00690-z
|View full text |Cite
|
Sign up to set email alerts
|

TEASER: early and accurate time series classification

Abstract: Early time series classification (eTSC) is the problem of classifying a time series after as few measurements as possible with the highest possible accuracy. The most critical issue of any eTSC method is to decide when enough data of a time series has been seen to take a decision: Waiting for more data points usually makes the classification problem easier but delays the time in which a classification is made; in contrast, earlier classification has to cope with less input data, often leading to inferior accur… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
38
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
3
2
1

Relationship

1
5

Authors

Journals

citations
Cited by 41 publications
(38 citation statements)
references
References 40 publications
(67 reference statements)
0
38
0
Order By: Relevance
“…This confidence is then used in the triggering mechanism. SR-CF (Mori et al 2018), TEASER (Schäfer and Leser 2020) and ECEC (Lv et al 2019) are all extensions of this idea using slightly different triggering mechanisms and prefix-classifiers. EARLIEST (Hartvigsen et al 2019) is based on a recurrent neural network (RNN) with LSTM cells.…”
Section: Earlytsc Methodsmentioning
confidence: 99%
See 3 more Smart Citations
“…This confidence is then used in the triggering mechanism. SR-CF (Mori et al 2018), TEASER (Schäfer and Leser 2020) and ECEC (Lv et al 2019) are all extensions of this idea using slightly different triggering mechanisms and prefix-classifiers. EARLIEST (Hartvigsen et al 2019) is based on a recurrent neural network (RNN) with LSTM cells.…”
Section: Earlytsc Methodsmentioning
confidence: 99%
“…To address the second question, we compared our multi-objective approach with a method that optimises a single objective (we refer to this baseline as SO-all). For this objective, we chose the harmonic mean of earliness and accuracy as suggested by Schäfer and Leser (2020):…”
Section: Baselinesmentioning
confidence: 99%
See 2 more Smart Citations
“…An interesting perspective on our Tier 2 is that it actually solves an early time series classification (eTSC) problem, for which there exist several mature approaches, e.g. TEASER (Schäfer and Leser, 2020) or ECTS (Xing et al, 2012). However, there exists a key difference that prevents us from using such methods directly: An eSPD System never classifies a chat as nongrooming as long as there are still messages left (or expected), while an eTSC system at some stage might decide that it is safe to stop controlling the chat (Loyola et al, 2018).…”
Section: Related Workmentioning
confidence: 99%