Long sequence time-series forecasting with deep learning: A survey

计算机科学 现存分类群 领域(数学) 深度学习 数据挖掘 人工智能 序列(生物学) 钥匙(锁) 机器学习 数据科学 数学 计算机安全 遗传学 进化生物学 生物 纯数学
作者
Zonglei Chen,Minbo Ma,Tianrui Li,Hongjun Wang,Chongshou Li
出处
期刊:Information Fusion [Elsevier BV]
卷期号:97: 101819-101819 被引量:118
标识
DOI:10.1016/j.inffus.2023.101819
摘要

The development of deep learning technology has brought great improvements to the field of time series forecasting. Short sequence time-series forecasting no longer satisfies the current research community, and long-term future prediction is becoming the hotspot, which is noted as long sequence time-series forecasting (LSTF). The LSTF has been widely studied in the extant literature, but few reviews of its research development are reported. In this article, we provide a comprehensive survey of LSTF studies with deep learning technology. We propose rigorous definitions of LSTF and summarize the evolution in terms of a proposed taxonomy based on network structure. Next, we discuss three key problems and corresponding solutions from long dependency modeling, computation cost, and evaluation metrics. In particular, we propose a Kruskal–Wallis test based evaluation method for evaluation metrics problems. We further synthesize the applications, datasets, and open-source codes of LSTF. Moreover, we conduct extensive case studies comparing the proposed Kruskal–Wallis test based evaluation method with existing metrics and the results demonstrate the effectiveness. Finally, we propose potential research directions in this rapidly growing field. All resources and codes are assembled and organized under a unified framework that is available online at https://github.com/Masterleia/TSF_LSTF_Compare.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
小赵发布了新的文献求助10
2秒前
3秒前
oky完成签到 ,获得积分10
3秒前
4秒前
fish完成签到 ,获得积分10
4秒前
曲艺发布了新的文献求助10
5秒前
5秒前
古夕完成签到,获得积分10
6秒前
7秒前
8秒前
8秒前
10秒前
lm发布了新的文献求助10
10秒前
无所归兮应助曲艺采纳,获得10
11秒前
11秒前
yar应助alone采纳,获得30
12秒前
za==应助小赵采纳,获得10
12秒前
13秒前
豆芽发布了新的文献求助10
13秒前
oky发布了新的文献求助10
13秒前
wdy111应助迷路硬币采纳,获得20
15秒前
15秒前
16秒前
艺高人胆大鸡腿完成签到 ,获得积分10
19秒前
乐乐应助焦糖采纳,获得10
19秒前
科研通AI2S应助nalan采纳,获得10
20秒前
静_完成签到 ,获得积分10
20秒前
20秒前
雪白元蝶发布了新的文献求助10
21秒前
21秒前
21秒前
留白完成签到 ,获得积分10
22秒前
共享精神应助小圆采纳,获得10
22秒前
22秒前
慕青应助梵高的向日葵采纳,获得10
22秒前
SYLH应助科研通管家采纳,获得20
22秒前
czh应助科研通管家采纳,获得10
22秒前
22秒前
ding应助科研通管家采纳,获得10
22秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Economic Geography and Public Policy 900
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988786
求助须知:如何正确求助?哪些是违规求助? 3531116
关于积分的说明 11252493
捐赠科研通 3269766
什么是DOI,文献DOI怎么找? 1804771
邀请新用户注册赠送积分活动 881870
科研通“疑难数据库(出版商)”最低求助积分说明 809021