.png){kind=icon}
返回
Automatic time series forecasting model design based on pruning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2024.111804.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Automatic Time Series Forecasting (TSF) model design which aims to help users to efficiently design forecasting models for the given time series data scenarios, is a novel research topic to be urgently solved. In this paper, we propose AutoTS algorithm trying to utilize the existing design skills and design efficient search methods to effectively solve this problem. In AutoTS, we extract effective design experience from the existing TSF works. We allow the effective combination of design experience from different sources, to create an effective search space containing a variety of TSF models to support different TSF tasks. Considering the huge search space, in AutoTS, we propose a two -stage pruning strategy to reduce the search difficulty and improve the search efficiency. Specifically, at the beginning of the search phase, we apply the vertical pruning method to quickly optimize each module of the TSF model. In the middle of the search, we turn to apply the horizontal pruning method to filter out less effective options for each module according to the learned experience and optimize modules. Moreover, in AutoTS, we introduce the knowledge graph to reveal associations between module options. We make full use of this relational information to learn higher -level features of each module option, to further improve the search quality. Experimental results show that AutoTS is well -suited for the TSF area. On some general datasets, our algorithms discover the assembled model with better performance. Additionally, compared to existing NAS algorithms, they exhibit higher search efficiency and greater search potential than the manually designed ones.
Keyword:
Time series forecasting model
Automated machine learning
Search space pruning
Neural architecture search
期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
机构
引用论文
Two-Phase Evolutionary Convolutional Neural Network Architecture Search for Medical Image Classification用于医学图像分类的两阶段进化卷积神经网络结构搜索.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏Phase Equilibria of Semiclathrate Hydrates of CO2, N2, CH4, or H2+ Tetra-n-butylammonium Bromide Aqueous SolutionPhase Equilibria of Semiclathrate Hydrates of CO2, N2, CH4,或H2 四-n-丁基溴化铵水溶液
PREAI
分享收藏Attention-Enabled Lightweight Neural Network Architecture for Detection of Action Unit ActivationIEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAI分享收藏Purification and characterization of oxidoreductases-catalyzing carbonyl reduction of the tobacco-specific nitrosamine 4-methylnitrosamino-1-(3-pyridyl)-1-butanone (NNK) in human liver cytosol人肝细胞溶胶中烟草特异性亚硝胺4-甲基亚硝基氨基-1-(3-吡啶基)-1-丁酮 (NNK) 的氧化还原酶催化羰基还原的纯化和表征Xenobiotica
XenobioticaIF0
PREAI
分享收藏 没有更多内容
没有更多内容