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A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting [PDF]
Energies, 2015 Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used ...
Francisco Martínez-Álvarez +3 more
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Data Mining Smart Energy Time Series [PDF]
Database Systems Journal, 2015 With the advent of smart metering technology the amount of energy data will increase significantly and utilities industry will have to face another big challenge - to find relationships within time-series data and even more - to analyze such huge numbers
Janina POPEANGA
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International Journal of Business Analytics, 2014 Modern technologies have allowed for the amassment of data at a rate never encountered before. Organizations are now able to routinely collect and process massive volumes of data. A plethora of regularly collected information can be ordered using an appropriate time interval. The data would thus be developed into a time series.
Daniel Hebert +3 more
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Time Series Data Mining for Sport Data: a Review
International Journal of Computer Science in Sport, 2022 Time series data mining deals with extracting useful and meaningful information from time series data. Recently, the increasing use of temporal data, in particular time series data, has received much attention in the literature. Since most of sports data
Komitova Rumena +3 more
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ACM Computing Surveys, 2012 In almost every scientific field, measurements are performed over time. These observations lead to a collection of organized data called time series . The purpose of time-series data mining is to try to extract all meaningful knowledge from the shape of data.
Esling, Philippe, Agon, Carlos
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THE ALGORITHM OF PIECEWISE CONSTANT TELEMETRIC PARAMETERS SEGMENTATION [PDF]
Vestnik KRAUNC: Fiziko-Matematičeskie Nauki, 2010 This paper is devoted to the modification of optimal segmentation algorithm and using it to telemetric signals ...
Vitol A.D., Zhukova N.A., Tristanov A.B.
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On causality of extreme events [PDF]
PeerJ, 2016 Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time.
Massimiliano Zanin
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Anomaly Detection Paradigm for Multivariate Time Series Data Mining for Healthcare
Applied Sciences, 2022 Time series data are significant, and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies.
Abdul Razaque +6 more
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Time Series Symbolization Method for the Data Mining K-Means Algorithm
Discrete Dynamics in Nature and Society, 2023 Time series is a data type frequently encountered in data analysis. With the current depth and breadth of the data and the improvement in computer processing capabilities, the dimensionality and the complexity of time series are getting higher and higher.
Guisheng Wang
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A Tri-Model Prediction Approach for COVID-19 ICU Bed Occupancy: A Case Study
Algorithms, 2023 The impact of COVID-19 and the pressure it exerts on health systems worldwide motivated this study, which focuses on the case of Greece. We aim to assist decision makers as well as health professionals, by estimating the short to medium term needs in ...
Nikolaos Stasinos +7 more
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