Cleaning Sensor Data in Smart Heating Control System

Zymbler, Mikhail; Kraeva, Yana; Latypova, Elizaveta; Kumar, Sachin; Shnayder, Dmitry A.; Basalaev, Alexander

doi:10.1109/glosic50886.2020.9267813

Cited by 5 publications

(2 citation statements)

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“…In this section, we apply PALMAD to discover subsequence anomalies in real-world time series from a smart heating control system. The PolyTER system [63] allows for intelligent monitoring and control of the operating conditions of utility systems through the analysis of the data from various IoT sensors installed in university campus buildings. We took a time series from a temperature sensor installed in a lecture hall and discovered the anomalies in a specified range.…”

Section: Case Studymentioning

confidence: 99%

High-Performance Time Series Anomaly Discovery on Graphics Processors

Zymbler

Kraeva

2023

Mathematics

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Currently, discovering subsequence anomalies in time series remains one of the most topical research problems. A subsequence anomaly refers to successive points in time that are collectively abnormal, although each point is not necessarily an outlier. Among numerous approaches to discovering subsequence anomalies, the discord concept is considered one of the best. A time series discord is intuitively defined as a subsequence of a given length that is maximally far away from its non-overlapping nearest neighbor. Recently introduced, the MERLIN algorithm discovers time series discords of every possible length in a specified range, thereby eliminating the need to set even that sole parameter to discover discords in a time series. However, MERLIN is serial, and its parallelization could increase the performance of discord discovery. In this article, we introduce a novel parallelization scheme for GPUs called PALMAD, parallel arbitrary length MERLIN-based anomaly discovery. As opposed to its serial predecessor, PALMAD employs recurrent formulas we have derived to avoid redundant calculations, and advanced data structures for the efficient implementation of parallel processing. Experimental evaluation over real-world and synthetic time series shows that our algorithm outperforms parallel analogs. We also apply PALMAD to discover anomalies in a real-world time series, employing our proposed discord heatmap technique to illustrate the results.

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Section: Case Studymentioning

confidence: 99%

High-Performance Time Series Anomaly Discovery on Graphics Processors

Zymbler

Kraeva

2023

Mathematics

Self Cite

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“…In this section, we apply PALMAD to discover subsequence anomalies in a real-world time series from a smart heating control system. The PolyTER system [64] allows for intelligent monitoring and control of operating conditions of utility systems through the analysis of the data from various IoT sensors installed in the university campus buildings. We took a time series from a temperature sensor installed in a lecture hall and discovered the anomalies in a specified range.…”

Section: Case Studymentioning

confidence: 99%

Fast Summarization of Long Time Series with Graphics Processor

Zymbler

Goglachev

2022

Mathematics

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Summarization of a long time series often occurs in analytical applications related to decision-making, modeling, planning, and so on. Informally, summarization aims at discovering a small-sized set of typical patterns (subsequences) to briefly represent the long time series. Apparent approaches to summarization like motifs, shapelets, cluster centroids, and so on, either require training data or do not provide an analyst with information regarding the fraction of the time series that a typical subsequence found corresponds to. Recently introduced, the time series snippet concept overcomes the above-mentioned limitations. A snippet is a subsequence that is similar to many other subsequences of the time series with respect to a specially defined similarity measure based on the Euclidean distance. However, the original Snippet-Finder algorithm has cubic time complexity concerning the lengths of the time series and the snippet. In this article, we propose the PSF (Parallel Snippet-Finder) algorithm that accelerates the original snippet discovery schema with GPU and ensures acceptable performance over very long time series. As opposed to the original algorithm, PSF splits the calculation of the similarity of all the time series subsequences to a snippet into several steps, each of which is performed in parallel. Experimental evaluation over real-world time series shows that PSF outruns both the original algorithm and a straightforward parallelization.

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