  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
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. Moreover, we
pre-process the original time series by partitioning it into segments with
various lengths which have similar total variation. The experimental study
shows that the proposed algorithm can achieve competitive compression ratio on
real-world time series.
