cleanTS: Automated (AutoML) tool to clean univariate time series at microscales

Shende, Mayur Kishor; Feijóo, Andrés; Bokde, Neeraj Dhanraj

doi:10.1016/j.neucom.2022.05.057

Cited by 14 publications

(12 citation statements)

References 43 publications

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“…ARDA [36], an example of a data acquisition technique that relies on integrating data from multiple sources . data cleaning methods [49].…”

Section: Data Integrationmentioning

confidence: 99%

“…Data preprocessing functions consist of a set of basic operations that transform raw data into a form that is useful for the machine learning model. Important preprocessing subtasks include data cleaning [61], [62], labeling or relabeling [53], [63], [63], [64], categorical encoding [65], [66], [67] and imputation of missing data [68], [69], [70]. Figure 8 depicts the main categories of data preprocessing tasks and the set of common problems they commonly tackle.…”

Section: The Concept Of Data Preprocessingmentioning

confidence: 99%

“…More recently, methods based on AutoML techniques [62], [68], [79], [89] have emerged as the most important class of methods for automating data imputation. Teague [68], for example, develops a tool based on AutoML technique to automatically impute missing tabular data.…”

Section: Automated Data Imputationmentioning

confidence: 99%

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“…ARDA [36], an example of a data acquisition technique that relies on integrating data from multiple sources . data cleaning methods [49].…”

Section: Data Integrationmentioning

confidence: 99%

Section: The Concept Of Data Preprocessingmentioning

confidence: 99%

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Data augmentation: A comprehensive survey of modern approaches

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“…Following several established research works from the literature, proposing a new softcomputing methodology must be validated with real time series datasets [39][40][41][42]. The proposed package in the current research was examined on six different time series dataset.…”

Section: Demonstrationmentioning

confidence: 99%

Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package

et al. 2022

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Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.

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“…Currently, anomaly detection has become an active research topic in the field of data mining and is widely used in areas such as healthcare, aerospace and industrial production [ 6 , 7 , 8 , 9 , 10 , 11 ]. Although numerous time series anomaly detection methods have been developed for univariate time series [ 1 , 12 , 13 , 14 ], where the anomalies are detected mainly based on one specific metric, for a complex real-world system, there is an intrinsic correlation between different sensors. A single univariate time series does not represent the overall state of the system well.…”

Section: Introductionmentioning

confidence: 99%

GTAD: Graph and Temporal Neural Network for Multivariate Time Series Anomaly Detection

Guan

Zhao

Dong

et al. 2022

Entropy

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The rapid development of smart factories, combined with the increasing complexity of production equipment, has resulted in a large number of multivariate time series that can be recorded using sensors during the manufacturing process. The anomalous patterns of industrial production may be hidden by these time series. Previous LSTM-based and machine-learning-based approaches have made fruitful progress in anomaly detection. However, these multivariate time series anomaly detection algorithms do not take into account the correlation and time dependence between the sequences. In this study, we proposed a new algorithm framework, namely, graph attention network and temporal convolutional network for multivariate time series anomaly detection (GTAD), to address this problem. Specifically, we first utilized temporal convolutional networks, including causal convolution and dilated convolution, to capture temporal dependencies, and then used graph neural networks to obtain correlations between sensors. Finally, we conducted sufficient experiments on three public benchmark datasets, and the results showed that the proposed method outperformed the baseline method, achieving detection results with F1 scores higher than 95% on all datasets.

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cleanTS: Automated (AutoML) tool to clean univariate time series at microscales

Cited by 14 publications

References 43 publications

Data augmentation: A comprehensive survey of modern approaches

Data augmentation: A comprehensive survey of modern approaches

Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package

GTAD: Graph and Temporal Neural Network for Multivariate Time Series Anomaly Detection

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