scholarly journals Highly Comparative Feature-Based Time-Series Classification

2014 ◽  
Vol 26 (12) ◽  
pp. 3026-3037 ◽  
Author(s):  
Ben D. Fulcher ◽  
Nick S. Jones
Keyword(s):  
Time Series ◽  
Time Series Classification ◽  
Comparative Feature ◽  
Feature Based

scholarly journals Development of Indicator of Data Sufficiency for Feature-based Early Time Series Classification with Applications of Bearing Fault Diagnosis

Processes ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 790
Author(s):  
Gilseung Ahn ◽  
Hwanchul Lee ◽  
Jisu Park ◽  
Sun Hur
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Early Time ◽  
Time Series Classification ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Benchmark Datasets ◽  
Feature Based ◽  
General Time

Diagnosis of bearing faults is crucial in various industries. Time series classification (TSC) assigns each time series to one of a set of pre-defined classes, such as normal and fault, and has been regarded as an appropriate approach for bearing fault diagnosis. Considering late and inaccurate fault diagnosis may have a significant impact on maintenance costs, it is important to classify bearing signals as early and accurately as possible. TSC, however, has a major limitation, which is that a time series cannot be classified until the entire series is collected, implying that a fault cannot be diagnosed using TSC in advance. Therefore, it is important to classify a partially collected time series for early time series classification (ESTC), which is a TSC that considers both accuracy and earliness. Feature-based TSCs can handle this, but the problem is to determine whether a partially collected time series is enough for a decision that is still unsolved. Motivated by this, we propose an indicator of data sufficiency to determine whether a feature-based fault detection classifier can start classifying partially collected signals in order to diagnose bearing faults as early and accurately as possible. The indicator is trained based on the cosine similarity between signals that were collected fully and partially as input to the classifier. In addition, a parameter setting method for efficiently training the indicator is also proposed. The results of experiments using four benchmark datasets verified that the proposed indicator increased both accuracy and earliness compared with the previous time series classification method and general time series classification.

Feature-Based Time Series Classification for Service Request Opening Prediction in the Telecom Industry

2019 ◽  
pp. 120-132
Author(s):  
Fabíola S. F. Pereira ◽  
André C. P. L. F. Carvalho ◽  
Rafael Assis ◽  
Maxley Costa ◽  
Elaine R. Faria ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Classification ◽  
Service Request ◽  
Feature Based ◽  
Telecom Industry

scholarly journals PFC: A Novel Perceptual Features-Based Framework for Time Series Classification

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1059
Author(s):  
Shaocong Wu ◽  
Xiaolong Wang ◽  
Mengxia Liang ◽  
Dingming Wu
Keyword(s):  
Time Series ◽  
Decision Trees ◽  
Morphological Structure ◽  
Structural Features ◽  
Gradient Boosting ◽  
Time Series Classification ◽  
Training Time ◽  
Perceptual Features ◽  
Data Points ◽  
Feature Based

Time series classification (TSC) is a significant problem in data mining with several applications in different domains. Mining different distinguishing features is the primary method. One promising method is algorithms based on the morphological structure of time series, which are interpretable and accurate. However, existing structural feature-based algorithms, such as time series forest (TSF) and shapelet traverse, all features through many random combinations, which means that a lot of training time and computing resources are required to filter meaningless features, important distinguishing information will be ignored. To overcome this problem, in this paper, we propose a perceptual features-based framework for TSC. We are inspired by how humans observe time series and realize that there are usually only a few essential points that need to be remembered for a time series. Although the complex time series has a lot of details, a small number of data points is enough to describe the shape of the entire sample. First, we use the improved perceptually important points (PIPs) to extract key points and use them as the basis for time series segmentation to obtain a combination of interval-level and point-level features. Secondly, we propose a framework to explore the effects of perceptual structural features combined with decision trees (DT), random forests (RF), and gradient boosting decision trees (GBDT) on TSC. The experimental results on the UCR datasets show that our work has achieved leading accuracy, which is instructive for follow-up research.

Detecting asthma control level using feature-based time series classification

2021 ◽  
pp. 107694
Author(s):  
Roghaye Khasha ◽  
Mohammad Mehdi Sepehri ◽  
Nasrin Taherkhani
Keyword(s):  
Time Series ◽  
Asthma Control ◽  
Control Level ◽  
Time Series Classification ◽  
Feature Based

scholarly journals Feature extraction by grammatical evolution for one-class time series classification

2021 ◽  
Author(s):  
Stefano Mauceri ◽  
James Sweeney ◽  
Miguel Nicolau ◽  
James McDermott
Keyword(s):  
Time Series ◽  
Classification Problem ◽  
Classification Performance ◽  
Grammatical Evolution ◽  
Data Driven ◽  
Time Series Classification ◽  
Class Time ◽  
Feature Based ◽  
Representation Of Time

AbstractWhen dealing with a new time series classification problem, modellers do not know in advance which features could enable the best classification performance. We propose an evolutionary algorithm based on grammatical evolution to attain a data-driven feature-based representation of time series with minimal human intervention. The proposed algorithm can select both the features to extract and the sub-sequences from which to extract them. These choices not only impact classification performance but also allow understanding of the problem at hand. The algorithm is tested on 30 problems outperforming several benchmarks. Finally, in a case study related to subject authentication, we show how features learned for a given subject are able to generalise to subjects unseen during the extraction phase.

A Frequent Pattern Based Time Series Classification Framework

2010 ◽  
Vol 32 (2) ◽  
pp. 261-266
Author(s):  
Li Wan ◽  
Jian-xin Liao ◽  
Xiao-min Zhu ◽  
Ping Ni
Keyword(s):  
Time Series ◽  
Frequent Pattern ◽  
Time Series Classification ◽  
Classification Framework
Sign in / Sign up

Export Citation Format

Share Document