A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …
generated in great volume by both physical sensors and online processes (virtual sensors) …
Bake off redux: a review and experimental evaluation of recent time series classification algorithms
In 2017, a research paper (Bagnall et al. Data Mining and Knowledge Discovery 31 (3): 606-
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …
660.) compared 18 Time Series Classification (TSC) algorithms on 85 datasets from the …
Deep contrastive representation learning with self-distillation
Recently, contrastive learning (CL) is a promising way of learning discriminative
representations from time series data. In the representation hierarchy, semantic information …
representations from time series data. In the representation hierarchy, semantic information …
Deep learning for time series classification and extrinsic regression: A current survey
Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …
learning tasks. Deep learning has revolutionized natural language processing and computer …
Densely knowledge-aware network for multivariate time series classification
Multivariate time series classification (MTSC) based on deep learning (DL) has attracted
increasingly more research attention. The performance of a DL-based MTSC algorithm is …
increasingly more research attention. The performance of a DL-based MTSC algorithm is …
MultiRocket: multiple pooling operators and transformations for fast and effective time series classification
We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-
of-the-art accuracy with a tiny fraction of the time and without the complex ensembling …
of-the-art accuracy with a tiny fraction of the time and without the complex ensembling …
DA-Net: Dual-attention network for multivariate time series classification
Multivariate time series classification is one of the increasingly important issues in machine
learning. Existing methods focus on establishing the global long-range dependencies or …
learning. Existing methods focus on establishing the global long-range dependencies or …
Hydra: Competing convolutional kernels for fast and accurate time series classification
We demonstrate a simple connection between dictionary methods for time series
classification, which involve extracting and counting symbolic patterns in time series, and …
classification, which involve extracting and counting symbolic patterns in time series, and …
Improving position encoding of transformers for multivariate time series classification
Transformers have demonstrated outstanding performance in many applications of deep
learning. When applied to time series data, transformers require effective position encoding …
learning. When applied to time series data, transformers require effective position encoding …
Dynamic sparse network for time series classification: Learning what to “see”
The receptive field (RF), which determines the region of time series to be “seen” and used, is
critical to improve the performance for time series classification (TSC). However, the …
critical to improve the performance for time series classification (TSC). However, the …