Deep Time-Series Clustering: A Review

Alqahtani, Ali; Ali, Mohammed; Xie, Xianghua; Jones, Mark W.

doi:10.3390/electronics10233001

Cited by 54 publications

(33 citation statements)

References 205 publications

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“…Cakupan pada penelitian ini berfokus pada analisis time series dan penerapannya pada deep learning dan data spasial seismik. Penelitian ini memperhatikan kesamaan antara sumber literatur dengan penelitian yang dilakukan, hal yang diperhatikan adalah langkah pengambilan data time series, proses analisis time series, kesamaan data dan penggunaan algoritma penelitian, dan variasi penggunaan algoritma (Alqahtani et al, 2021).…”

Section: Cakupan Reviewunclassified

Analisis Time Series Untuk Deep Learning Dan Prediksi Data Spasial Seismik: Studi Literatur

Cahyo

Susanti

2023

Jurtek

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Time series analysis is a deep learning method to process data stored in a dense time. This method works for forecast data in particular cases, such as to analyze and provide the output of a trend from the data. Time series analysis can be used to analyze trends in natural disasters such as earthquakes in Indonesia. As technology advances today, in addition to using seismographs or tools to predict seismic phenomena on the earth's surface, time series analysis can be used to make forecasts and predictions. This paper will summarize various literature reviews about time series analysis and previous research to predict seismic spatial data over the last ten years. The goal is to afford the several approaches or algorithms to be able to forecast seismic spatial data to increase awareness. The results of this literature study were used to find trends, state of the arts, and research challenges and develop new models or methods to predict seismic spatial data. The study shows that deep learning methods can achieve better accurate performance in processing seismic spatial data and other complex data than conventional methods. The deep learning methods can use Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU), linear regression, and Artificial Neural Network (ANN).

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Section: Cakupan Reviewunclassified

Analisis Time Series Untuk Deep Learning Dan Prediksi Data Spasial Seismik: Studi Literatur

Cahyo

Susanti

2023

Jurtek

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“…Feature-based approaches convert underlying data into informative features through feature extraction [27], dimension reduction [9], or representation of time series [28,29]. The extracted features are then given to clustering algorithms using the common distance metric.…”

Section: Related Workmentioning

confidence: 99%

Accelerated univariate sequential clustering

Enayati

Mortazavi

Basiri

2022

Preprint

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Data clustering is an important task in the field of data mining. In many real applications, clustering algorithms must consider the order of data, resulting in the sequential clustering problem. For instance, analyzing the moving pattern of an object and detecting community structure in a complex network are related to sequential clustering. The constraint of the continuous region prevents previous clustering algorithms from being directly applied to the problem. A dynamic programming algorithm was proposed to address the issue, which returns the optimal sequential clustering. However, it is not scalable. This paper addresses the issue via a greedy stopping condition that prevents the algorithm from continuing to search when it's likely that the best solution has been found. Experimental results on multiple datasets show that the algorithm is much faster than its original solution while the optimality gap is negligible.

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“…NILM is based on a variety of techniques [4], which have proven to be effective at various degrees in a domestic context [5,6]; they are then considered for extensions to completely different applications, electrified railways, exploring the most promising signal features. The analysis is extended to both time-and frequency-domain data, considering voltage and current waveforms, instantaneous power, power trajectory, harmonic components, and harmonic quantities.…”

Section: Introductionmentioning

confidence: 99%

“…NILM relies heavily on modern machine learning (ML) approaches [1,[3][4][5], as well as more traditional techniques [2,15,24,25]. ML methods can be classified into supervised and unsupervised methods, using both deterministic and statistic approaches, whereas traditional techniques are based on more direct inspections of "electrical" quantities, such as voltagecurrent trajectories, active-reactive power plots, displacement factor discrimination, etc.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Monitoring Applied to AC Railways

Mariscotti

2022

Energies

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Non-intrusive load monitoring takes place in residential and industrial contexts to disaggregate and identify loads connected to a distribution grid. This work studies the applicability and effectiveness for AC railways, considering the highly dynamic behavior of rolling stock as an electric load, immersed in varying contexts of moving loads. Both voltage–current diagrams and harmonic spectra were considered for identification and extraction of features relevant to classification and clustering. Principal components were extracted, approaching the problem using principal component analysis (PCA) and partial least square regression (PLSR). Clustering methods were then discussed, verifying separability performance and applicability to the railway context, checking the performance by means of the balanced accuracy index. Based on more than one hundred measured spectra, PLSR has been confirmed with superior performance and lower complexity. Independent verification based on dispersion and correlation were used to spot relevant spectrum components to use as clustering features and confirm the PLSR outcome.

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Deep Time-Series Clustering: A Review

Cited by 54 publications

References 205 publications

Analisis Time Series Untuk Deep Learning Dan Prediksi Data Spasial Seismik: Studi Literatur

Analisis Time Series Untuk Deep Learning Dan Prediksi Data Spasial Seismik: Studi Literatur

Accelerated univariate sequential clustering

Non-Intrusive Load Monitoring Applied to AC Railways

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