Deep Dict: Deep Learning-based Lossy Time Series Compressor for IoT Data
Jinxin Liu Petar Djukic Michel Kulhandjian Burak Kantarci
We propose Deep Dict, a deep learning-based lossy time series compressor
designed to achieve a high compression ratio while maintaining decompression
error within a predefined range. Deep Dict incorporates two essential
components: the Bernoulli transformer autoencoder (BTAE) and a distortion
constraint. BTAE extracts Bernoulli representations from time series data,
reducing the size of the representations compared to conventional autoencoders.
The distortion constraint limits the prediction error of BTAE to the desired
range. Moreover, in order to address the limitations of common regression
losses such as L1/L2, we introduce a novel loss function called quantized
entropy loss (QEL). QEL takes into account the specific characteristics of the
problem, enhancing robustness to outliers and alleviating optimization
challenges. Our evaluation of Deep Dict across ten diverse time series datasets
from various domains reveals that Deep Dict outperforms state-of-the-art lossy
compressors in terms of compression ratio by a significant margin by up to
53.66%.