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Bootstrap–CURE: A Novel Clustering Approach for Sensor Data— An Application to 3D Printing Industry

Shikha Suman, Ashutosh Karna, Karina Gibert
2022 Applied Sciences  
An automatic technique to detect the suitable number of clusters using the dendrogram is developed.  ...  A real application is presented to illustrate the performance and usefulness of the proposal. In addition, a new state of the art for sensor data analysis is presented.  ...  In [37] , an application from the iron and steel industries processes sensor data through a discrete-time extended Kalman filter for both process state estimation and sensor data fusion.  ... 
doi:10.3390/app12042191 fatcat:gue7dadi6fdynp6g63sij5nk6e

An Application of Sensor and Streaming Analytics to Oil Production

Krishnamurthy Viswanathan, Chetan Gupta, Choudur Lakshminarayan, Ming C. Hao, Umeshwar Dayal, Ravigopal Vennelakanti, Paul Helm, Sumitha Rangaiah, Harikrishnam-Raju Sagiraju, Sunil Doddmani
2011 International Conference on Management of Data  
Live OI system also supports querying of historical data to find past occurrences of patterns and suggested actions, and a dashboard for humans to monitor and interact with the operational system.  ...  At HP Labs, we are building "Live Operational Intelligence (Live OI) System"a system that ingests streams of operational data generated by multiple sources such as sensors and operational logs, and provides  ...  For instance, to build a rational analytics solution sometime requires combining textual and time series data to obtain a data set in the appropriate time sequence.  ... 
dblp:conf/comad/ViswanathanGLHDVHRSD11 fatcat:5zlypay3hvfedhbciwv763w6ri


Luiz Fernando Ferreira Gomes de Assis, João Porto de Albuquerque, Benjamin Herfort, Enrico Steiger, Flávio Eduardo Aoki Horita
2018 Revista Brasileira de Cartografia  
This paper presents an approach for the automated geographic prioritization of social network messages for fl ood risk management based on sensor data streams.  ...  The results revealed that the proposed approach is useful for identifying valuable fl ood-related messages in near real-time.  ...  We would like to thank São Paulo Research Foundation (FAPESP) by the grant #13/16202-1 and #14/08398-6, and the National Council for Scientific and Technological Development (CNPq) by the grant #477499  ... 
doaj:9c1ac5a530e2417abc2d09824e316d48 fatcat:3ltmuiyiffaaxjmz2n2er5tu3i

TSML (Time Series Machine Learning)

2020 JuliaCon Proceedings  
Inherent in this automation is the installation of sensor networks for status monitoring and data collection.  ...  To address these issues, we developed TSML. Its technology is based on using the pipeline of lightweight filters as building blocks to process huge amount of industrial time series data in parallel.  ...  Rapid deployment of these sensors result to many of them not properly labeled or classified. Time series classification is a significant first step for optimal prediction and anomaly detection.  ... 
doi:10.21105/jcon.00051 fatcat:y6vwkcxokbeexkltk4msw35bpe

TSML (Time Series Machine Learnng) [article]

Paulito Palmes, Joern Ploennigs, Niall Brady
2020 arXiv   pre-print
Inherent in this automation is the installation of sensor networks for status monitoring and data collection.  ...  To address these issues, we developed TSML. Its technology is based on using the pipeline of lightweight filters as building blocks to process huge amount of industrial time series data in parallel.  ...  Rapid deployment of these sensors result to many of them not properly labeled or classified. Time series classification is a significant first step for optimal prediction and anomaly detection.  ... 
arXiv:2005.13191v1 fatcat:xlzyerwvdbdfnbeh5qgeubmmma

Sales Anomaly Detection Using Automatic Time Series Decomposition

Nevie Chrislie Kinzonzi Ngongo, Oscar Famous Darteh
2022 Journal of Economics Management and Trade  
In this study, an empirical approach based on automatic time series decomposition (ATSD) was used to detect anomalies in sales data.  ...  This study proposes an automated time series decomposition (ATSD) technique for sales anomaly detection using weekly ice cream sale data from Google Trends with the caption "(Mobile AL-Pensacola (Ft.  ...  This paper aims to detect anomalous data based on the automatic time series decomposition.  ... 
doi:10.9734/jemt/2022/v28i930434 fatcat:d64db5mwefh2hi6pkq6wc3tvw4

Time Series Segmentation through Automatic Feature Learning [article]

Wei-Han Lee, Jorge Ortiz, Bongjun Ko, Ruby Lee
2018 arXiv   pre-print
These data can be used to build automatic labeling algorithms that produce labels as an expert would. Here, we refer to human-specified boundaries as breakpoints.  ...  of our algorithm for practical applications.  ...  INTRODUCTION Changepoint detection is an important, fundamental technique used in the analysis of time series data.  ... 
arXiv:1801.05394v2 fatcat:yyilwwwk3ffkvoff7rcfiwbgdy

Pattern recognition in multivariate time series

Stephan Spiegel, Brijnesh Johannes Jain, Ernesto William De Luca, Sahin Albayrak
2011 Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management - PIKM '11  
This dissertation proposal aims at developing and investigating efficient methods for the recognition of contextual patterns in multivariate time series in different application domains based on machine  ...  Furthermore we describe a number of applications, where pattern recognition in multivariate time series is practical or rather necessary.  ...  A decade ago, DTW was introduced to the data mining community as a utility for various tasks for time series problems including clustering, classification, and anomaly detection [6] .  ... 
doi:10.1145/2065003.2065011 dblp:conf/cikm/SpiegelJLA11 fatcat:nlyhocoq6va6lcoczhj3rub6xy

Toolkit for Time Series Anomaly Detection

Dhaval Patel, Dzung Phan, Markus Mueller, Amaresh Rajasekharan
2022 Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining  
This tutorial presents a design and implementation of a scikit-compatible system for detecting anomalies from time series data for the purpose of offering a broad range of algorithms to the end user, with  ...  Compared to traditional MLPipe, the anomaly detection pipeline for time series data differs in various ways: (1) unsupervised model training; (2) real valued anomaly scores to label generation scheme;  ...  Patel is an expert in Data Mining, Machine Learning, Time Series Data Analysis, etc.  ... 
doi:10.1145/3534678.3542625 fatcat:zpgradijo5g7dc3sbejot35eau

Anomaly Detection in Time Series for Smart Agriculture

Vladislav Bína, Jitka Bartošová, Vladimír Přibyl
2022 International Journal of Management, Knowledge and Learning  
Purpose: Enormous amounts of data from sensors and automatic systems is available to contemporary managers of farms and agricultural enterprises.  ...  In connection to the extremely fast development in data and information spheres in smart agriculture within the last two decades develop also the new approaches for prediction in time series and search  ...  The sensors can provide a vast amount of data which is feasible to analyse only in an automated manner and to find outliers or anomalies which usually present some events of interest.  ... 
doi:10.53615/2232-5697.11.177-186 fatcat:bqsz7byiv5aafpq4zdwwrpltgq

Change Point Enhanced Anomaly Detection for IoT Time Series Data

Elena-Simona Apostol, Ciprian-Octavian Truică, Florin Pop, Christian Esposito
2021 Water  
Due to the exponential growth of the Internet of Things networks and the massive amount of time series data collected from these networks, it is essential to apply efficient methods for Big Data analysis  ...  Additionally, we propose a new confidence metric based on the support for a time series point to be an anomaly and the support for the same point to be a change point.  ...  Time Series Outlier Detection An outlier or an anomaly is a data point that significantly differs from other observations in a time series.  ... 
doi:10.3390/w13121633 fatcat:mji72vblmjcwrpl3vtse7pbhny

A Review on Anomaly Detection in Time Series

2021 International Journal of Advanced Trends in Computer Science and Engineering  
Among others, it is very simple to obtain time series data from a variety of various science and finance applications and an anomaly detection technique for time series is becoming a very prominent research  ...  In this article we will first define what an anomaly in time series is, and then describe quickly some of the methods suggested in the past two or three years for detection of anomaly in time series  ...  Usman Ahmed Raza, our faculty member, who encouraged us and supported us to achieve this goal.  ... 
doi:10.30534/ijatcse/2021/571032021 fatcat:5alfes5srzakzdvfgxtdniyn3q

Streaming Pattern Discovery in Multiple Time-Series

Spiros Papadimitriou, Jimeng Sun, Christos Faloutsos
2005 Very Large Data Bases Conference  
Moreover, it is any-time, single pass, and it dynamically detects changes.  ...  The discovered trends can also be used to immediately spot potential anomalies, to do efficient forecasting and, more generally, to dramatically simplify further data processing.  ...  We wish to thank Michael Bigrigg for providing the temperature sensor data.  ... 
dblp:conf/vldb/PapadimitriouSF05 fatcat:fw5mcj443vep7ax32lm5gtgjra

Correction of Outliers in Temperature Time Series Based on Sliding Window Prediction in Meteorological Sensor Network

Li Ma, Xiaodu Gu, Baowei Wang
2017 Information  
In order to detect outliers in temperature time series data for improving data quality and decision-making quality related to design and operation, we proposed an algorithm based on sliding window prediction  ...  The experimental results show that the proposed algorithm can not only effectively detect outliers in the time series of meteorological data but also improves the correction efficiency notoriously. frequency  ...  This paper presents an algorithm for outlier detection of temperature time series based on sliding window prediction in the meteorological sensor network.  ... 
doi:10.3390/info8020060 fatcat:mp7rtfg3enghfjdjacudntqkfm

Generic and Scalable Framework for Automated Time-series Anomaly Detection

Nikolay Laptev, Saeed Amizadeh, Ian Flint
2015 Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15  
This paper introduces a generic and scalable framework for automated anomaly detection on large scale time-series data.  ...  We compare our approach against other anomaly detection systems on real and synthetic data with varying time-series characteristics.  ...  Fast and efficient identification of these outliers is useful for many applications including: intrusion detection, credit card fraud, sensor events, medical diagnoses, law enforcement and others [1]  ... 
doi:10.1145/2783258.2788611 dblp:conf/kdd/LaptevAF15 fatcat:ix4naujr7vgbzhxvowuhrt4ur4
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