Benchmarking Deep Learning for Time Series: Challenges and Directions

被引：0

作者：

Huang, Xinyuan ^{[1
]}

Fox, Geoffrey C. ^{[2
]}

Serebryakov, Sergey ^{[3
]}

Mohan, Ankur ^{[4
]}

Morkisz, Pawel ^{[5
,6
]}

Dutta, Debojyoti ^{[1
]}

机构：

[1] Cisco Syst, San Jose, CA 95134 USA

[2] Indiana Univ, Bloomington, IN USA

[3] Hewlett Packard Enterprise, San Jose, CA USA

[4] In Q Tel, Arlington, VA USA

[5] Nvidia, Warsaw, Poland

[6] AGH Univ Sci & Technol, Krakow, Poland

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA) | 2019年

关键词：

machine learning; deep learning; time series; performance; benchmark; CLASSIFICATION;

D O I：

10.1109/bigdata47090.2019.9005496

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Deep learning for time series is an emerging area with close ties to industry, yet under represented in performance benchmarks for machine learning systems. In this paper, we present a landscape of deep learning applications applied to time series, and discuss the challenges and directions towards building a robust performance benchmark of deep learning workloads for time series data.

引用

页码：5679 / 5682

页数：4

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