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LFZip: Lossy Compression of Multivariate Floating-Point Time Series Data via Improved Prediction

Abstract: 

Time series data compression is emerging as an important problem with the growth in IoT devices and sensors. Due to the presence of noise in these datasets, lossy compression can often provide significant compression gains without impacting the performance of downstream applications. In this work, we propose an error-bounded lossy compressor, LFZip, for multivariate floating-point time series data that provides guaranteed reconstruction up to user-specified maximum absolute error. The compressor is based on the prediction-quantization-entropy coder framework and benefits from improved prediction using linear models and neural networks. We evaluate the compressor on several time series datasets where it outperforms the existing state-of-the-art error-bounded lossy compressors. The code and data are available at https://github.com/shubhamchandak94/LFZip.

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Paper Details

Authors:
Kedar Tatwawadi, Chengtao Wen, Lingyun Wang, Juan Aparicio, Tsachy Weissman
Submitted On:
18 March 2020 - 5:02pm
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Shubham Chandak
Paper Code:
111
Session:
Session 11
Document Year:
2020
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Document Files

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[1] Kedar Tatwawadi, Chengtao Wen, Lingyun Wang, Juan Aparicio, Tsachy Weissman, "LFZip: Lossy Compression of Multivariate Floating-Point Time Series Data via Improved Prediction", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4997. Accessed: Aug. 13, 2020.
@article{4997-20,
url = {http://sigport.org/4997},
author = {Kedar Tatwawadi; Chengtao Wen; Lingyun Wang; Juan Aparicio; Tsachy Weissman },
publisher = {IEEE SigPort},
title = {LFZip: Lossy Compression of Multivariate Floating-Point Time Series Data via Improved Prediction},
year = {2020} }
TY - EJOUR
T1 - LFZip: Lossy Compression of Multivariate Floating-Point Time Series Data via Improved Prediction
AU - Kedar Tatwawadi; Chengtao Wen; Lingyun Wang; Juan Aparicio; Tsachy Weissman
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4997
ER -
Kedar Tatwawadi, Chengtao Wen, Lingyun Wang, Juan Aparicio, Tsachy Weissman. (2020). LFZip: Lossy Compression of Multivariate Floating-Point Time Series Data via Improved Prediction. IEEE SigPort. http://sigport.org/4997
Kedar Tatwawadi, Chengtao Wen, Lingyun Wang, Juan Aparicio, Tsachy Weissman, 2020. LFZip: Lossy Compression of Multivariate Floating-Point Time Series Data via Improved Prediction. Available at: http://sigport.org/4997.
Kedar Tatwawadi, Chengtao Wen, Lingyun Wang, Juan Aparicio, Tsachy Weissman. (2020). "LFZip: Lossy Compression of Multivariate Floating-Point Time Series Data via Improved Prediction." Web.
1. Kedar Tatwawadi, Chengtao Wen, Lingyun Wang, Juan Aparicio, Tsachy Weissman. LFZip: Lossy Compression of Multivariate Floating-Point Time Series Data via Improved Prediction [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4997