Summary of Abba-vsm: Time Series Classification Using Symbolic Representation on the Edge, by Meerzhan Kanatbekova et al.
ABBA-VSM: Time Series Classification using Symbolic Representation on the Edge
by Meerzhan Kanatbekova, Shashikant Ilager, Ivona Brandic
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Edge AI framework is proposed for Time Series Classification (TSC) in resource-constrained environments, featuring the Adaptive Brownian Bridge-based Symbolic Aggregation Vector Space Model (ABBA-VSM). The ABBA-VSM compresses raw time series data into symbolic representations, capturing changing trends and reducing communication and computation demands. The model achieves up to 80% compression ratio and 90-100% accuracy for binary classification, and average compression ratio of 60% and accuracy ranging from 60-80% for non-binary classification. This approach enables the deployment of TSC services on Edge devices, providing privacy-enabled and latency-sensitive solutions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Edge AI is used in many industries like environmental monitoring and smart city management. It helps process IoT data and provides fast and private services. But current Time Series Classification (TSC) algorithms need a lot of computing power and data to work well. This makes them hard to use on devices with limited resources, like Edge devices. The new Adaptive Brownian Bridge-based Symbolic Aggregation Vector Space Model (ABBA-VSM) helps solve this problem by compressing raw time series data into simpler representations that can be processed quickly. |
Keywords
» Artificial intelligence » Classification » Time series » Vector space
