Time series classification based on temporal features

Ji, Cun; Du, Mingsen; Hu, Yupeng; Liu, Shijun; Pan, Li; Zheng, Xiangwei

doi:10.1016/j.asoc.2022.109494

Applied Soft Computing

2022

DOI: 10.1016/j.asoc.2022.109494

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Time series classification based on temporal features

Cun Ji

Mingsen Du²,

Yupeng Hu³

et al.

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Cited by 23 publications

(1 citation statement)

References 36 publications

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“…Pei et al [22] presented a hidden-unit logistic model (HULM) to construct the latent structure within the data. Well-known feature-based algorithms include the learned pattern similarity [23], bag of SFA symbols (BOSS) [24], time series forest [25], TS-CHIEF [26], temporal feature fusion [27], and fuzzy cognitive map [28].…”

mentioning

confidence: 99%

DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification

Xiao,

Xu,

Xing

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

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This paper proposes a dual-network-based feature extractor, perceptive capsule network (PCapN), for multivariate time series classification (MTSC), including a local feature network (LFN) and a global relation network (GRN). The LFN has two heads (i.e., Head A and Head B), each containing two squash CNN blocks and one dynamic routing block to extract the local features from the data and mine the connections among them. The GRN consists of two capsule-based transformer blocks and one dynamic routing block to capture the global patterns of each variable and correlate the useful information of multiple variables. Unfortunately, it is difficult to directly deploy PCapN on mobile devices due to its strict requirement for computing resources. So, this paper designs a lightweight capsule network (LCapN) to mimic the cumbersome PCapN. To promote knowledge transfer from PCapN to LCapN, this paper proposes a deep transformer capsule mutual (DTCM) distillation method. It is targeted and offline, using one-and two-way operations to supervise the knowledge distillation process for the dualnetwork-based student and teacher models. Experimental results show that the proposed PCapN and DTCM achieve excellent performance on UEA2018 datasets regarding top-1 accuracy.

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mentioning

confidence: 99%

DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification

Xiao,

Xu,

Xing

et al. 2024

IEEE Trans. Cogn. Dev. Syst.

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Time Series Classification Based on Adaptive Feature Adjustment and Multi-scale AGRes2Net

Peng

Cai

et al. 2023

Neural Process Lett

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Fast sharpness-aware training for periodic time series classification and forecasting

Park

Kim

Choi

et al. 2023

Applied Soft Computing

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Time series classification based on temporal features

Cited by 23 publications

References 36 publications

DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification

DTCM: Deep Transformer Capsule Mutual Distillation for Multivariate Time Series Classification

Time Series Classification Based on Adaptive Feature Adjustment and Multi-scale AGRes2Net

Fast sharpness-aware training for periodic time series classification and forecasting

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