Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder

Time series account for a large proportion of the data stored in financial, medical and scientific databases. The efficient storage of time series is important in practical applications. In this paper, we propose a novel lossy compression scheme for time series. The encoder and decoder are both composed by recurrent neural networks (RNN) such as long short-term memory (LSTM). There is an autoencoder between encoder and decoder, which encodes the hidden state and input together and decodes them at the decoder side. The input window size is adaptively changing based on the local statistics of time series. The experimental study shows that the proposed algorithm can achieve competitive compression ratio on real-world time series.