Streaming time series retrieval (TSR) has been widely concerned in academia and industry. Considering the large volume, high dimensionality and continuous accumulation features of time series, there is limited capability to perform in-depth similarity searching directly on the raw time series data. Therefore, time series representation, which can provide the dimension reduction-based approximate results for the raw data, should be utilized in the first step for streaming TSR. However, the existing representation-based TSR methods mainly have two limitations: on the one hand, the representation efficiency of the current methods is too slow to adapt for real-time streaming time series representation; on the other hand, the retrieval efficiency of them is also not ideal, and thus fails to recognize the specific given sequence patterns on the streaming data effectively. In this paper, we present an efficient retrieval method on streaming time series. Concretely, our method can incrementally represent the features of streaming data to automatically prune the corresponding dissimilar sequences and retain the most similar candidates for efficient one-pass searching. Extensive experiments on real world datasets have been conducted to demonstrate the superiority of our method.

中文翻译：

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流时间序列检索的多分辨率表示

流式时间序列检索（TSR）在学术界和工业界受到广泛关注。考虑到时间序列的大容量、高维度和连续积累的特点，直接对原始时间序列数据进行深度相似性搜索的能力有限。因此，可以在流式 TSR 的第一步中使用可以为原始数据提供基于降维的近似结果的时间序列表示。然而，现有的基于表示的TSR方法主要有两个局限性：一方面，当前方法的表示效率太慢，无法适应实时流式时间序列表示；另一方面，它们的检索效率也不理想，因此无法有效地识别流数据上的特定给定序列模式。在本文中，我们提出了一种有效的流时间序列检索方法。具体来说，我们的方法可以增量表示流数据的特征，以自动修剪相应的不相似序列并保留最相似的候选序列以进行有效的一次性搜索。已经对现实世界的数据集进行了广泛的实验，以证明我们方法的优越性。