Symbolic Aggregate approXimation (SAX) is a common dimensionality reduction approach for time-series data which has been employed in a variety of domains, including classification and anomaly detection in time-series data. Domains also include shape recognition where the shape outline is converted into time-series data forinstance epoch classification of archived arrowheads. In this paper we propose a dimensionality reduction and shape recognition approach based on the SAX algorithm, an application which requires responses on cost efficient, IoT-like, platforms. The challenge is largely dealing with the computational expense of the SAX algorithm in IoT-like applications, from simple time-series dimension reduction through shape recognition. The approach is based on lowering the dimensional space while capturing and preserving the most representative features of the shape. We present three scenarios of increasing computational complexity backing up our statements with measurement of performance characteristics

翻译：符号聚合近似（SAX）是一种常见的时间序列数据降维方法，已广泛应用于多个领域，包括时间序列数据的分类与异常检测。其应用领域亦涵盖形状识别，例如将形状轮廓转换为时间序列数据以实现对存档箭镞的时代分类。本文提出一种基于SAX算法的降维与形状识别方法，该方法需在成本效益高、类物联网平台上实现响应。核心挑战在于应对SAX算法在类物联网应用中的计算开销问题，涵盖从简单时间序列降维到形状识别的全过程。本方法通过降低维度空间同时捕捉并保留形状最具代表性的特征来实现目标。我们通过性能特征测量验证了三种计算复杂度递增的应用场景，以支撑本文论点。