.png){kind=icon}
返回
Implementing transfer learning across different datasets for time series forecasting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2020.107617.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Due to the extensive practical value of time series prediction, many excellent algorithms have been proposed. Most of these methods are developed assuming that massive labeled training data are available. However, this assumption might be invalid in some actual situations. To address this limitation, a transfer learning framework with deep architectures is proposed. Since convolutional neural network (CNN) owns favorable feature extraction capability and can implement parallelization more easily, we propose a deep transfer learning method resorting to the architecture of CNN, termed as DTr-CNN for short. It can effectively alleviate the available labeled data absence and leverage useful knowledge to the current prediction. Notably, in our method, transfer learning process is implemented across different datasets. For a given target domain, in real-world scenarios, relativity of truly available potential source datasets may not be obvious, which is challenging and rarely referred to in most existing time series prediction methods. Aiming at this problem, the incorporation of Dynamic Time Warping (DTW) and Jensen-Shannon (JS) divergence is adopted for the selection of the appropriate source domain. Effectiveness of the proposed method is empirically underpinned by the experiments conducted on one group of synthetic and two groups of practical datasets. Besides, an additional experiment on NN5 dataset is conducted. (C) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Time series prediction
Deep learning
Transfer learning
Convolutional neural network (CNN)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
暂无机构信息
引用论文
Chaotic time series prediction with residual analysis method using hybrid Elman-NARX neural networks.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction用于时间序列预测的Elman递归神经网络的合作协同进化中的竞争与合作
PREAI
分享收藏分享收藏Transfer learning-based discriminative correlation filter for visual trackingPATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏Opinion of the Scientific Panel on additives and products or substances used in animal feed (FEEDAP) on the safety of the product “Biomin BBSH 797” for piglets, pigs for fattening and chickens for fatteningEFSA Journal
EFSA JournalIF0
PREAI
分享收藏
查看更多内容