Pattern Identification for State Prediction in Dynamic Data Streams
| Autor: | Alireza Ahrabian, Shirin Enshaeifar, Payam Barnaghi, Seyed Amir Hoseinitabatabaei |
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| Rok vydání: | 2017 |
| Předmět: |
Approximation theory
Group method of data handling Computer science Dynamic data Approximation algorithm 020206 networking & telecommunications 02 engineering and technology computer.software_genre Data segment Data-driven Data aggregator 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm design Data mining computer |
| Zdroj: | iThings/GreenCom/CPSCom/SmartData |
| DOI: | 10.1109/ithings-greencom-cpscom-smartdata.2017.122 |
| Popis: | This work proposes a pattern identification and online prediction algorithm for processing Internet of Things (IoT) time-series data. This is achieved by first proposing a new data aggregation and data driven discretisation method that does not require data segment normalisation. We apply a dictionary based algorithm in order to identify patterns of interest along with prediction of the next pattern. The performance of the proposed method is evaluated using synthetic and real-world datasets. The evaluations results shows that our system is able to identify the patterns by up to 85% accuracy which is 16.5% higher than a baseline using the Symbolic Aggregation Approximation (SAX) method. |
| Databáze: | OpenAIRE |
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