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

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.

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Health Monitoring of Hydraulic System Using Feature-based Multivariate Time-series Classification Model

Lecture Notes in Electrical Engineering - Security and Privacy ◽

10.1007/978-981-33-6781-4_7 ◽

2021 ◽

pp. 81-92

Author(s):

Ananda Shivani Medishetty ◽

Navya Sahithi Muthavarapu ◽

Sai Goutham Goli ◽

B Sirisha ◽

B Sandhya

Keyword(s):

Time Series ◽

Health Monitoring ◽

Hydraulic System ◽

Multivariate Time Series ◽

Classification Model ◽

Time Series Classification ◽

Feature Based

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A Fast Shapelet Discovery Algorithm Based on Important Data Points

International Journal of Web Services Research ◽

10.4018/ijwsr.2017040104 ◽

2017 ◽

Vol 14 (2) ◽

pp. 67-80 ◽

Cited By ~ 12

Author(s):

Cun Ji ◽

Chao Zhao ◽

Li Pan ◽

Shijun Liu ◽

Chenglei Yang ◽

...

Keyword(s):

Time Series ◽

Classification Accuracy ◽

Time Series Classification ◽

Important Data ◽

The Past ◽

Significant Interest ◽

Data Points ◽

Discovery Time ◽

Accuracy Rates

Time series classification (TSC) has attracted significant interest over the past decade. A shapelet is one fragment of a time series that can represent class characteristics of the time series. A classifier based on shapelets is interpretable, more accurate, and faster. However, the time it takes to find shapelets is enormous. This article will propose a fast shapelet (FS) discovery algorithm based on important data points (IDPs). First, the algorithm will identify IDPs. Next, the subsequence containing one or more IDPs will be selected as a candidate shapelet. Finally, the best shapelets will be selected. Results will show that the proposed algorithm reduces the shapelet discovery time by approximately 14.0% while maintaining the same level of classification accuracy rates.

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

Processes ◽

10.3390/pr8070790 ◽

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.

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Time Series Classification with InceptionFCN

Sensors ◽

10.3390/s22010157 ◽

2021 ◽

Vol 22 (1) ◽

pp. 157

Author(s):

Saidrasul Usmankhujaev ◽

Bunyodbek Ibrokhimov ◽

Shokhrukh Baydadaev ◽

Jangwoo Kwon

Keyword(s):

Time Series ◽

Deep Learning ◽

Deep Neural Networks ◽

Learning Approaches ◽

Time Series Classification ◽

Convolutional Network ◽

Training Time ◽

Learning Techniques ◽

Significant Research ◽

Previous State

Deep neural networks (DNN) have proven to be efficient in computer vision and data classification with an increasing number of successful applications. Time series classification (TSC) has been one of the challenging problems in data mining in the last decade, and significant research has been proposed with various solutions, including algorithm-based approaches as well as machine and deep learning approaches. This paper focuses on combining the two well-known deep learning techniques, namely the Inception module and the Fully Convolutional Network. The proposed method proved to be more efficient than the previous state-of-the-art InceptionTime method. We tested our model on the univariate TSC benchmark (the UCR/UEA archive), which includes 85 time-series datasets, and proved that our network outperforms the InceptionTime in terms of the training time and overall accuracy on the UCR archive.

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