Image de couverture de livre

CAAP: Class-Dependent Automatic Data Augmentation Based On Adaptive Policies For Time Series

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Chang, Tien-Yu, Dai, Hao, Tseng, Vincent S.
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Integrating Inductive Biases in Transformers via Distillation for Financial Time Series Forecasting
par: Den, Yu-Chen, et autres
Publié: (2026)

An Efficient Data Reuse with Tile-Based Adaptive Stationary for Transformer Accelerators
par: Li, Tseng-Jen, et autres
Publié: (2025)

OATS: Online Data Augmentation for Time Series Foundation Models
par: Deng, Junwei, et autres
Publié: (2026)

LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters
par: Chang, Ching, et autres
Publié: (2023)

Class-Dependent Hybrid Data Augmentation for Multiclass Migraine Classification under Severe Class Imbalance
par: Somón, Elvin, et autres
Publié: (2026)

Class-Based Time Series Data Augmentation to Mitigate Extreme Class Imbalance for Solar Flare Prediction
par: Wen, Junzhi, et autres
Publié: (2024)

Class-Dependent Perturbation Effects in Evaluating Time Series Attributions
par: Baer, Gregor, et autres
Publié: (2025)

Time Series Data Augmentation as an Imbalanced Learning Problem
par: Cerqueira, Vitor, et autres
Publié: (2024)

PromptTSS: A Prompting-Based Approach for Interactive Multi-Granularity Time Series Segmentation
par: Chang, Ching, et autres
Publié: (2025)

Evaluation of Machine and Deep Learning Techniques for Cyclone Trajectory Regression and Status Classification by Time Series Data
par: Lo, Ethan Zachary, et autres
Publié: (2025)

MIRA: Medical Time Series Foundation Model for Real-World Health Data
par: Li, Hao, et autres
Publié: (2025)

Data Augmentation for Multivariate Time Series Classification: An Experimental Study
par: Ilbert, Romain, et autres
Publié: (2024)

Data Augmentation in Time Series Forecasting through Inverted Framework
par: Tan, Hongming, et autres
Publié: (2025)

Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigation
par: Baer, Gregor, et autres
Publié: (2025)

Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting
par: Hu, Yifan, et autres
Publié: (2024)

TimeDRL: Disentangled Representation Learning for Multivariate Time-Series
par: Chang, Ching, et autres
Publié: (2023)

Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data
par: Dohi, Kota, et autres
Publié: (2024)

Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion
par: Wang, Xinyan, et autres
Publié: (2025)

Cast-R1: Learning Tool-Augmented Sequential Decision Policies for Time Series Forecasting
par: Tao, Xiaoyu, et autres
Publié: (2026)

TimeRAF: Retrieval-Augmented Foundation model for Zero-shot Time Series Forecasting
par: Zhang, Huanyu, et autres
Publié: (2024)

MedM2T: A MultiModal Framework for Time-Aware Modeling with Electronic Health Record and Electrocardiogram Data
par: Kuo, Yu-Chen, et autres
Publié: (2025)

Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey
par: Gao, Zijun, et autres
Publié: (2023)

MGTS-Net: Exploring Graph-Enhanced Multimodal Fusion for Augmented Time Series Forecasting
par: Hao, Shule, et autres
Publié: (2025)

Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
par: Yu, Guoqi, et autres
Publié: (2024)

TADA: Temporal Adversarial Data Augmentation for Time Series Data
par: Lee, Byeong Tak, et autres
Publié: (2024)

Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series
par: Chang, Ching, et autres
Publié: (2024)

Parametric Augmentation for Time Series Contrastive Learning
par: Zheng, Xu, et autres
Publié: (2024)

Exploiting the Prior of Generative Time Series Imputation
par: Miao, YuYang, et autres
Publié: (2025)

SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation
par: Ryu, Hyun, et autres
Publié: (2023)

Routing Channel-Patch Dependencies in Time Series Forecasting with Graph Spectral Decomposition
par: Li, Dongyuan, et autres
Publié: (2026)

A Policy Gradient-Based Sequence-to-Sequence Method for Time Series Prediction
par: Sima, Qi, et autres
Publié: (2024)

Time-Series Classification with Multivariate Statistical Dependence Features
par: Sun, Yao, et autres
Publié: (2026)

Automatically Adaptive Conformal Risk Control
par: Blot, Vincent, et autres
Publié: (2024)

AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis
par: Yu, Han, et autres
Publié: (2024)

SDE: A Simplified and Disentangled Dependency Encoding Framework for State Space Models in Time Series Forecasting
par: Weng, Zixuan, et autres
Publié: (2024)

One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data
par: Luetto, Simone, et autres
Publié: (2023)

LangTime: A Language-Guided Unified Model for Time Series Forecasting with Proximal Policy Optimization
par: Niu, Wenzhe, et autres
Publié: (2025)

FeatNavigator: Automatic Feature Augmentation on Tabular Data
par: Liang, Jiaming, et autres
Publié: (2024)

TimeDP: Learning to Generate Multi-Domain Time Series with Domain Prompts
par: Huang, Yu-Hao, et autres
Publié: (2025)

DAD4TS: Data-Augmentation-Oriented Diffusion Model for Time-Series Forecasting with Small-Scale Data
par: Suzuki, Masahiro, et autres
Publié: (2026)