因此,许多工作开始关注利用多变量之间的相关性来提高异常检测的准确性。Jones等人提取时间序列的统计和平滑轨迹(SST)特征,利用一组非线性函数对相关变量进行建模以检测异常。MAD-GAN利用LSTM网络作为基础模型捕捉时间序列数据的时间相关性,考虑到不同时间序列变量之间的复杂依赖关系,提出了一种结合GANs的无监督异常检测方法...
ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification 【论文地址】 https://arxiv.org/abs/2405.14608 【论文源码】 https://github.com/xuanmay2701/shapeformer 论文背景 时间序列分类在时间序列分析领域是一个基础和...
(1)输入部分,time series的每个time step的features 相当于一个句子里的一个token的embedding,但是根据实际经验来看,如果每个timestep的features太少做self attention效果不好,这里作者提供的方法是直接用一个shared的linear层来做升维的操作,看了下源代码确实是这么设计的https://github.com/gzerveas/mvts_transformer/...
论文标题:ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification 论文链接:https://arxiv.org/abs/2405.14608 代码链接:https://github.com/xuanmay2701/shapeformer. 前言 本文面向的任务是多元时间序列分类任务,提出Shapelet Transformer(ShapeFormer),提取通用特征和代表性类别特定特征(shapelets)...
一、背景 使用transdormer来进行多变量时间序列分类。提出了GTN网络。 二、模型 使用了双塔式 的transformer结构,这是因为在多变量的时间序列中,需要考虑...
Classifying solar flares is essential for understanding their impact on space weather forecasting. We propose a novel approach using a multi-head attention and transformer mechanism to classify multivariate time series (MVTS) instances of photospheric magnetic field parameters of the flaring events in ...
论文题目:A transformer-based framework for multivariate time series representation learning(KDD 2021) 下载地址:https://arxiv.org/pdf/1912.09363.pdf 这篇文章借鉴了预训练语言模型Transformer的思路,希望能够在多元时间序列上通过无监督的方法,借助Transformer模型结构,学习良好的多元时间序列表示。本文重点在于针对多...
Frequency-domain MLPs are More Effective Lea深度之眼整理rners in Time Series Forecasting ICML 2023 Learning Deep Time-index Models for Time S深度之眼整理eries Forecasting KDD 2023 TSMixer: Lightweight MLP-Mixer Model fo深度之眼整理r Multivariate Time Series Forecasting ...
MSGNet: Learning Multi-Scale Inter-Serjes Correlations for Multivariate Time Series Forecasting NeurIPS2023 Frequency-domain MLPs are More Effective Lea深度之眼整理rners in Time Series Forecasting ICML 2023 Learning Deep Time-index Models for Time S深度之眼整理erie...
Transformers have achieved remarkable performance in multivariate time series(MTS) forecasting due to their capability to capture long-term dependencies. However, the canonical attention mechanism has two key limitations: (1) its quadratic time complexit