Multivariate Time Series Classification: A Deep Learning Approach

Mohamed Abouelnaga, Julien Vitay, Aida Farahani
7/5/2023
cs.LG

Official code: xuanmay2701/shapeformer (136 stars, MIT, last commit 2024-12-05). 1 more implementation below.

Abstract

This paper investigates different methods and various neural network architectures applicable in the time series classification domain. The data is obtained from a fleet of gas sensors that measure and track quantities such as oxygen and sound. With the help of this data, we can detect events such as occupancy in a specific environment. At first, we analyze the time series data to understand the effect of different parameters, such as the sequence length, when training our models. These models employ Fully Convolutional Networks (FCN) and Long Short-Term Memory (LSTM) for supervised learning and Recurrent Autoencoders for semisupervised learning. Throughout this study, we spot the differences between these methods based on metrics such as precision and recall identifying which technique best suits this problem.

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Code Implementations(2)

xuanmay2701/shapeformerOfficial100%
MIT

ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification

13630Python, TypeScriptJun 27, 20241 years agoMIT
WenjieDu/PyPOTSCommunity100%
PyTorchBSD-3-Clause

A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values

2,060190Shell, PythonMar 29, 20225 months agoBSD-3-Clause
anomaly-detectionclassificationclusteringdata-analysisdata-mining+10 more

Cite this paper

@article{abouelnaga2023multivariate,
  title  = {Multivariate Time Series Classification: A Deep Learning Approach},
  author = {Mohamed Abouelnaga and Julien Vitay and Aida Farahani},
  year   = {2023},
  eprint = {2307.02253},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.02253v1}
}

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