Data Mining and Knowledge Discovery最新文献_第10页

Improving position encoding of transformers for multivariate time series classification 多变量时间序列分类中变压器位置编码的改进

3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-09-05 DOI: 10.1007/s10618-023-00948-2

Navid Mohammadi Foumani, Chang Wei Tan, Geoffrey I. Webb, Mahsa Salehi

{"title":"Improving position encoding of transformers for multivariate time series classification","authors":"Navid Mohammadi Foumani, Chang Wei Tan, Geoffrey I. Webb, Mahsa Salehi","doi":"10.1007/s10618-023-00948-2","DOIUrl":"https://doi.org/10.1007/s10618-023-00948-2","url":null,"abstract":"Abstract Transformers have demonstrated outstanding performance in many applications of deep learning. When applied to time series data, transformers require effective position encoding to capture the ordering of the time series data. The efficacy of position encoding in time series analysis is not well-studied and remains controversial, e.g., whether it is better to inject absolute position encoding or relative position encoding, or a combination of them. In order to clarify this, we first review existing absolute and relative position encoding methods when applied in time series classification. We then proposed a new absolute position encoding method dedicated to time series data called time Absolute Position Encoding (tAPE). Our new method incorporates the series length and input embedding dimension in absolute position encoding. Additionally, we propose computationally Efficient implementation of Relative Position Encoding (eRPE) to improve generalisability for time series. We then propose a novel multivariate time series classification model combining tAPE/eRPE and convolution-based input encoding named ConvTran to improve the position and data embedding of time series data. The proposed absolute and relative position encoding methods are simple and efficient. They can be easily integrated into transformer blocks and used for downstream tasks such as forecasting, extrinsic regression, and anomaly detection. Extensive experiments on 32 multivariate time-series datasets show that our model is significantly more accurate than state-of-the-art convolution and transformer-based models. Code and models are open-sourced at https://github.com/Navidfoumani/ConvTran .","PeriodicalId":55183,"journal":{"name":"Data Mining and Knowledge Discovery","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135205529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Z-Time: efficient and effective interpretable multivariate time series classification Z-Time：高效有效的可解释多元时间序列分类

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-09-05 DOI: 10.1007/s10618-023-00969-x

Zed Lee, Tony Lindgren, P. Papapetrou

引用次数: 1

Studying bias in visual features through the lens of optimal transport 通过最优运输的视角研究视觉特征偏差

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-09-02 DOI: 10.1007/s10618-023-00972-2

Simone Fabbrizzi, Xuan Zhao, Emmanouil Krasanakis, Symeon Papadopoulos, Eirini Ntoutsi

引用次数: 1

Network embedding based on high-degree penalty and adaptive negative sampling 基于高度惩罚和自适应负采样的网络嵌入

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-09-02 DOI: 10.1007/s10618-023-00973-1

Gang-Feng Ma, Xu-Hua Yang, Wei Ye, Xinli Xu, Lei Ye

引用次数: 0

Improving neural network’s robustness on tabular data with D-layers 用d层提高神经网络对表格数据的鲁棒性

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-08-31 DOI: 10.1007/s10618-023-00965-1

Haiyang Xia, Nayyar Zaidi, Yishuo Zhang, Gang Li

引用次数: 0

Sky-signatures: detecting and characterizing recurrent behavior in sequential data 天空签名:在序列数据中检测和描述循环行为

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-08-29 DOI: 10.1007/s10618-023-00949-1

Clément Gautrais, Peggy Cellier, T. Guyet, R. Quiniou, A. Termier

引用次数: 0

SALτ: efficiently stopping TAR by improving priors estimates SALτ：通过改进先验估计有效地阻止TAR

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-08-28 DOI: 10.1007/s10618-023-00961-5

Alessio Molinari, Andrea Esuli

引用次数: 0

A semi-supervised interactive algorithm for change point detection 一种用于变点检测的半监督交互式算法

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-08-28 DOI: 10.1007/s10618-023-00974-0

Zhenxiang Cao, N. Seeuws, Marina De Vos, Alexander Bertrand

引用次数: 0

A tale of two roles: exploring topic-specific susceptibility and influence in cascade prediction 两个角色的故事:探索主题特异性敏感性和级联预测的影响

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-08-27 DOI: 10.1007/s10618-023-00953-5

Ninghan Chen, Xihui Chen, Zhiqiang Zhong, Jun Pang

引用次数: 0

Variable screening for Lasso based on multidimensional indexing 基于多维索引的Lasso变量筛选

IF 4.8 3区计算机科学

Data Mining and Knowledge Discovery Pub Date : 2023-08-27 DOI: 10.1007/s10618-023-00950-8

Barbara Żogała-Siudem, S. Jaroszewicz

引用次数: 0