Formalism for a multiresolution time series database model

Serra, Aleix Llusà; Vila-Marta, Sebastià; Escobet, Teresa

doi:10.1016/j.is.2015.08.006

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…This happens when some variables contain instantaneous information and other variables represent averages with different time spans, shifts or production batches. This problem can be solved using mutiresolution soft sensors and multiresolution time series models [45,46,47].…”

Section: Introductionmentioning

confidence: 99%

Knowledge based recursive non-linear partial least squares (RNPLS)

et al. 2020

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Soft sensors driven by data are very common in modern industry to predict critical variables which are difficult to measure using other variables that are relatively easier to obtain. The use of soft sensors implies some challenges, such as the predictor variables colinearity, the time-varying and possible non-linear nature of the industrial process. To deal with the first challenge, the partial least square (PLS) regression has been employed for many applications to deal with the linear variable relationships, with noisy and highly correlated data. However, the PLS model needs to deal with the other two issues: the non-linear and the time-varying side behaviour of the processes. In this work, a new knowledge based methodology for a recursive non-linear PLS algorithm (RNPLS) is systematized to deal with these issues. Here, the non-linear PLS algorithm is made by carrying out the PLS regression over the augmented matrix of input, which includes knowledge based non-linear transformations of some of the variables. This transformation depends on the type of the system, and takes into account the available knowledge about the process, which is provided by expert knowledge or em-

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Section: Introductionmentioning

confidence: 99%

Knowledge based recursive non-linear partial least squares (RNPLS)

et al. 2020

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Hierarchical Multiresolution Representation of Streaming Time Series

et al. 2021

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Time Series Management Systems: A Survey

Jensen

Pedersen

Thomsen

2017

IEEE Trans. Knowl. Data Eng.

137

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The collection of time series data increases as more monitoring and automation are being deployed. These deployments range in scale from an Internet of things (IoT) device located in a household to enormous distributed Cyber-Physical Systems (CPSs) producing large volumes of data at high velocity. To store and analyze these vast amounts of data, specialized Time Series Management Systems (TSMSs) have been developed to overcome the limitations of general purpose Database Management Systems (DBMSs) for times series management. In this paper, we present a thorough analysis and classification of TSMSs developed through academic or industrial research and documented through publications. Our classification is organized into categories based on the architectures observed during our analysis. In addition, we provide an overview of each system with a focus on the motivational use case that drove the development of the system, the functionality for storage and querying of time series a system implements, the components the system is composed of, and the capabilities of each system with regard to Stream Processing and Approximate Query Processing (AQP). Last, we provide a summary of research directions proposed by other researchers in the field and present our vision for a next generation TSMS.Comment: 20 Pages, 15 Figures, 2 Tables, Accepted for publication in IEEE TKD

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Formalism for a multiresolution time series database model

Cited by 4 publications

References 23 publications

Knowledge based recursive non-linear partial least squares (RNPLS)

Knowledge based recursive non-linear partial least squares (RNPLS)

Hierarchical Multiresolution Representation of Streaming Time Series

Time Series Management Systems: A Survey

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