TEST: Text prototype aligned embedding to activate LLM's ability for time series
This work summarizes two ways to accomplish Time-Series (TS) tasks in today's Large
Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a …
Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a …
MFGTN: A multi-modal fast gated transformer for identifying single trawl marine fishing vessel
Y Gu, Z Hu, Y Zhao, J Liao, W Zhang - Ocean Engineering, 2024 - Elsevier
In order to achieve sustainable development of marine fishery resources, effective supervise
of trawl fishing during forbidden fishing period is of great significance. This paper addresses …
of trawl fishing during forbidden fishing period is of great significance. This paper addresses …
Shapeformer: Shapelet transformer for multivariate time series classification
Multivariate time series classification (MTSC) has attracted significant research attention due
to its diverse real-world applications. Recently, exploiting transformers for MTSC has …
to its diverse real-world applications. Recently, exploiting transformers for MTSC has …
CATodyNet: Cross-attention temporal dynamic graph neural network for multivariate time series classification
H Gui, G Li, X Tang, J Lu - Knowledge-Based Systems, 2024 - Elsevier
Multivariate time series classification is widely applicable to finance, healthcare, and
meteorology; therefore, it is a valuable data-mining task. However, existing methods rely …
meteorology; therefore, it is a valuable data-mining task. However, existing methods rely …
TV-Net: Temporal-Variable feature harmonizing Network for multivariate time series classification and interpretation
Multivariate time series classification (MTSC), which identifies categories of multiple sensor
signals recorded in continuous time, is widely used in various fields such as transportation …
signals recorded in continuous time, is widely used in various fields such as transportation …
ST-Tree with interpretability for multivariate time series classification
Multivariate time series classification is of great importance in practical applications and is a
challenging task. However, deep neural network models such as Transformers exhibit high …
challenging task. However, deep neural network models such as Transformers exhibit high …
[HTML][HTML] MTS2Graph: Interpretable multivariate time series classification with temporal evolving graphs
Conventional time series classification approaches based on bags of patterns or shapelets
face significant challenges in dealing with a vast amount of feature candidates from high …
face significant challenges in dealing with a vast amount of feature candidates from high …
POCKET: Pruning random convolution kernels for time series classification from a feature selection perspective
In recent years, two competitive time series classification models, namely, ROCKET and
MINIROCKET, have garnered considerable attention due to their low training cost and high …
MINIROCKET, have garnered considerable attention due to their low training cost and high …
AutoLDT: a lightweight spatio-temporal decoupling transformer framework with AutoML method for time series classification
P Wang, K Wang, Y Song, X Wang - Scientific Reports, 2024 - nature.com
Time series classification finds widespread applications in civil, industrial, and military fields,
while the classification performance of time series models has been improving with the …
while the classification performance of time series models has been improving with the …
MagNet: Multilevel Dynamic Wavelet Graph Neural Network for Multivariate Time Series Classification
X Hong, J Hu, T Xu, X Ren, F Wu, X Ma… - ACM Transactions on …, 2024 - dl.acm.org
Multivariate Time Series Classification (MTSC) is a fundamental data mining task, which is
widely applied in the fields like health care and energy management. However, the existing …
widely applied in the fields like health care and energy management. However, the existing …