Time2State: An Unsupervised Framework for Inferring the Latent States in Time Series Data

Time series data from monitoring applications reflect the physical or logical states of the objects, which may produce time series of distinguishable characteristics in different states. Thus, time series data can usually be split into different segments, each reflecting a state of the objects. These states carry rich high-level semantic information, e.g., run, walk, or jump, which helps people better understand the behaviour of the monitored objects. Nevertheless, these states are latent and hard to discover, because the characteristic of time series is complicated and the computational cost is high. This paper develops an efficient and effective unsupervised approach for inferring the latent states of massive multivariate time data. To reduce the computational cost, we present Time2State, a scalable framework that utilizes a sliding window and an encoder to greatly reduce the length of raw time series. To train the encoder, we propose a novel unsupervised loss function, LSE-Loss. Extensive experiments show that compared to the state-of-the-art time series representation learning methods of the same kind, LSE-Loss brings a performance improvement of up to 15% in accuracy.