Time Series Compression Based on Adaptive Piecewise Recurrent Autoencoder
By Daniel Hsu. 2017
13 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Francisco Herrera
- Salvador Garcia
- M. J. Jesús
- Francisco Charte
- David Charte
- Alberto Fernández
- Enrique Herrera-Viedma
- Julián Luengo
In Signal Processing
- M. J. Jesús
- Francisco Herrera
- Francisco Charte
- David Charte
- Salvador Garcia
- Enrique Herrera-Viedma
- Francisco Chiclana
Citations in the corpus
- A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, methodology): “In [125] the APRA ( Adaptive Pairwise Recurrent Encoder ) model is presented, combining an AE and a LSTM to successfully compress this kind of information.”