A temporal convolutional recurrent autoencoder based framework for compressing time series data-Reference-Cited by-同舟云学术

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A temporal convolutional recurrent autoencoder based framework for compressing time series data

Published:2023-11 Issue: Volume:147 Page:110797
ISSN:1568-4946
Container-title:Applied Soft Computing
language:en
Short-container-title:Applied Soft Computing

Author:

Zheng Zhong^ORCID,Zhang Zijun^ORCID

Publisher

Elsevier BV

Subject

Software

Reference29 articles.

1. Capturing sensor-generated time series with quality guarantees;Lazaridis,2003

2. Time series compression based on adaptive piecewise recurrent autoencoder;Hsu,2017

3. Time series compression survey;Chiarot;ACM Comput. Surv.,2023

4. TRISTAN: Real-time analytics on massive time series using sparse dictionary compression;Marascu,2014

5. Corad: Correlation-aware compression of massive time series using sparse dictionary coding;Khelifati,2019

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1. Probabilistic load forecasting for integrated energy systems using attentive quantile regression temporal convolutional network;Advances in Applied Energy;2024-07

2. Cocv: A compression algorithm for time-series data with continuous constant values in IoT-based monitoring systems;Internet of Things;2024-04

3. Lossless Data Compression for Time-Series Sensor Data Based on Dynamic Bit Packing;Sensors;2023-10-19

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