Approaches and Applications of Early Classification of Time Series: A Review

Gupta, Ashish; Gupta, Hari Prabhat; Biswas, Bhaskar; Dutta, Tanima

doi:10.1109/tai.2020.3027279

Cited by 59 publications

(37 citation statements)

References 72 publications

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“…For example, paper [37] extracted a variety of spectral indices combined with DTW for classification. However, this method uses each time point as a vector to calculate its Euclidean distance in the DTW local distance, which is also DTW based on Euclidean distance (EDDTW), and assigns the same weight to each feature while ignoring the relationship between different features [38,39]. On the one hand, the time series with multiple features should be considered as a whole in order to maintain the correlation between features [40].…”

Section: Introductionmentioning

confidence: 99%

Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping

Wan

Liu

et al. 2022

Remote Sensing

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Efficient methodologies for vegetation-type mapping are significant for wetland’s management practices and monitoring. Nowadays, dynamic time warping (DTW) based on remote sensing time series has been successfully applied to vegetation classification. However, most of the previous related studies only focused on Normalized Difference Vegetation Index (NDVI) time series while ignoring multiple features in each period image. In order to further improve the accuracy of wetland vegetation classification, Mahalanobis Distance-based Dynamic Time Warping (MDDTW) using multi-dimensional feature time series was employed in this research. This method extends the traditional DTW algorithm based on single-dimensional features to multi-dimensional features and solves the problem of calculating similarity distance between multi-dimensional feature time series. Vegetation classification experiments were carried out in the Yellow River Delta (YRD). Compared with different classification methods, the results show that the K-Nearest Neighbors (KNN) algorithm based on MDDTW (KNN-MDDTW) has achieved better classification accuracy; the overall accuracy is more than 90%, and kappa is more than 0.9.

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Section: Introductionmentioning

confidence: 99%

Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping

Wan

Liu

et al. 2022

Remote Sensing

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show abstract

“…This paragraph provides an overview of the ECTS approaches. For a recent and more complete survey, the reader can refer to [2,3]. The pioneering approaches were based on some form of confidence criterion and waited until a predefined threshold was reached before triggering their decisions.…”

Section: A Short State Of the Art On Early Classification Of Time Seriesmentioning

confidence: 99%

Open challenges for Machine Learning based Early Decision-Making research

Bondu¹,

Achenchabe²,

Bifet³

et al. 2022

Preprint

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More and more applications require early decisions, i.e. taken as soon as possible from partially observed data. However, the later a decision is made, the more its accuracy tends to improve, since the description of the problem to hand is enriched over time. Such a compromise between the earliness and the accuracy of decisions has been particularly studied in the field of Early Time Series Classification. This paper introduces a more general problem, called Machine Learning based Early Decision Making (ML-EDM), which consists in optimizing the decision times of models in a wide range of settings where data is collected over time. After defining the ML-EDM problem, ten challenges are identified and proposed to the scientific community to further research in this area. These challenges open important application perspectives, discussed in this paper.

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“…Real-time identification allows DSOs to take immediate counteractions in cases where FAs could result in critical situations, such as congestions and under or overvoltages. Supervised detection or classification of time series events usually requires as input the entire time series sample [40]. For real-time event identification this becomes a fundamental problem.…”

Section: Real-time Identificationmentioning

confidence: 99%

“…For real-time event identification this becomes a fundamental problem. Existing early classification techniques come at the cost of decreased accuracy [40], and are not applicable to an OS classification problem. In the proposed pipeline the problem of prediction delay is addressed by separating event identification into two consecutive steps.…”

Section: Real-time Identificationmentioning

confidence: 99%

Unsupervised detection and open-set classification of fast-ramped flexibility activation events

Müller,

Heinrich,

Heussen

et al. 2021

Preprint

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The continuous electrification of the mobility and heating sectors adds much-needed flexibility to the power system. However, flexibility utilization also introduces new challenges to distribution system operators (DSOs), who need mechanisms to supervise flexibility activations and monitor their effect on distribution network operation. Flexibility activations can be broadly categorized to those originating from electricity markets and those initiated by the DSO to avoid constraint violations. Simultaneous electricity market driven flexibility activations may cause voltage quality or temporary overloading issues, and the failure of flexibility activations initiated by the DSO might leave critical grid states unresolved. This work proposes a novel data processing pipeline for automated real-time identification of fast-ramped flexibility activation events. Its practical value is twofold: i) potentially critical flexibility activations originating from electricity markets can be detected by the DSO at an early stage, and ii) successful activation of DSO-requested flexibility can be verified by the operator. In both cases the increased awareness would allow the DSO to take counteractions to avoid potentially critical grid situations. The proposed pipeline combines techniques from unsupervised detection and open-set classification. For both building blocks feasibility is systematically evaluated and proofed on real load and flexibility activation data.

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Approaches and Applications of Early Classification of Time Series: A Review

Cited by 59 publications

References 72 publications

Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping

Wetland Vegetation Classification through Multi-Dimensional Feature Time Series Remote Sensing Images Using Mahalanobis Distance-Based Dynamic Time Warping

Open challenges for Machine Learning based Early Decision-Making research

Unsupervised detection and open-set classification of fast-ramped flexibility activation events

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