ABBA-VSM：基于符号表示的边缘时间序列分类
ABBA-VSM: Time Series Classification using Symbolic Representation on the Edge
AI总结：
本文提出ABBA-VSM，一种基于自适应布朗桥符号聚合向量空间模型的边缘时间序列分类方法，通过符号压缩降低通信与计算开销，在UCR数据集上实现高达80%压缩率和90-100%二分类准确率。
摘要：
近年来，边缘人工智能在各行各业的应用日益普及，从环境监测到智慧城市管理。边缘人工智能促进了物联网（IoT）数据的处理，并利用机器学习（ML）算法（例如时间序列分类（TSC））为应用用户提供隐私保护和低延迟的服务。然而，现有的TSC算法需要访问完整的原始数据，并要求在运行时有效训练和使用它们需要大量的计算资源。这使得它们难以部署在资源受限的边缘环境中。为解决这一问题，本文提出了一种基于自适应布朗桥的符号聚合向量空间模型（ABBA-VSM）。这是一种专为边缘分类服务设计的新型TSC模型。在此，我们首先将原始时间序列自适应地压缩为符号表示，从而捕捉数据的变化趋势。随后，我们直接在这些符号上训练分类模型。ABBA-VSM减少了物联网与边缘设备之间的通信数据以及计算周期，有助于在边缘开发资源高效的TSC服务。我们使用UCR时间序列分类档案中的数据集进行了大量实验来评估我们的解决方案。结果表明，ABBA-VSM在二分类中实现了高达80%的压缩率和90-100%的准确率。而对于非二分类，它实现了平均60%的压缩率和60-80%的准确率。
英文摘要：
In recent years, Edge AI has become more prevalent with applications across various industries, from environmental monitoring to smart city management. Edge AI facilitates the processing of Internet of Things (IoT) data and provides privacy-enabled and latency-sensitive services to application users using Machine Learning (ML) algorithms, e.g., Time Series Classification (TSC). However, existing TSC algorithms require access to full raw data and demand substantial computing resources to train and use them effectively in runtime. This makes them impractical for deployment in resource-constrained Edge environments. To address this, in this paper, we propose an Adaptive Brownian Bridge-based Symbolic Aggregation Vector Space Model (ABBA-VSM). It is a new TSC model designed for classification services on Edge. Here, we first adaptively compress the raw time series into symbolic representations, thus capturing the changing trends of data. Subsequently, we train the classification model directly on these symbols. ABBA-VSM reduces communication data between IoT and Edge devices, as well as computation cycles, in the development of resource-efficient TSC services on Edge. We evaluate our solution with extensive experiments using datasets from the UCR time series classification archive. The results demonstrate that the ABBA-VSM achieves up to 80% compression ratio and 90-100% accuracy for binary classification. Whereas, for non-binary classification, it achieves an average compression ratio of 60% and accuracy ranging from 60-80%.
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