:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Lohse, Christopher, Wahl, Jonas
Format:	Preprint
Published:	2024
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/2407.13313
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

HADL Framework for Noise Resilient Long-Term Time Series Forecasting
by: Dey, Aditya, et al.
Published: (2025)

When Counterfactual Reasoning Fails: Chaos and Real-World Complexity
by: Aalaila, Yahya, et al.
Published: (2025)

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
by: Wang, Wenxuan, et al.
Published: (2025)

Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning
by: Wu, Shunyu, et al.
Published: (2025)

Clustering-based Anomaly Detection in Multivariate Time Series Data
by: Li, Jinbo, et al.
Published: (2025)

Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generation
by: Wang, Wenxuan, et al.
Published: (2025)

TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data
by: Taga, Ege Onur, et al.
Published: (2025)

Multi-Modal Forecaster: Jointly Predicting Time Series and Textual Data
by: Kim, Kai, et al.
Published: (2024)

Diffusion Transformers for Tabular Data Time Series Generation
by: Garuti, Fabrizio, et al.
Published: (2025)

TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
by: Gwiazda, Malgorzata, et al.
Published: (2026)

Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data
by: Cheng, Debo, et al.
Published: (2024)

Semantic Association Rule Learning from Time Series Data and Knowledge Graphs
by: Karabulut, Erkan, et al.
Published: (2023)

TADA: Temporal Adversarial Data Augmentation for Time Series Data
by: Lee, Byeong Tak, et al.
Published: (2024)

Interpreting Outliers in Time Series Data through Decoding Autoencoder
by: Knab, Patrick, et al.
Published: (2024)

Are Large Language Models Useful for Time Series Data Analysis?
by: Tang, Francis, et al.
Published: (2024)

Meta-learning to Address Data Shift in Time Series Classification
by: Myren, Samuel, et al.
Published: (2026)

A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
by: Hasan, Uzma, et al.
Published: (2023)

Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification
by: Crnovrsanin, Tarik, et al.
Published: (2024)

A Survey on Diffusion Models for Time Series and Spatio-Temporal Data
by: Yang, Yiyuan, et al.
Published: (2024)

SPEAR: Soft Prompt Enhanced Anomaly Recognition for Time Series Data
by: Wei, Hanzhe, et al.
Published: (2025)

TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
by: Li, Hongkai, et al.
Published: (2026)

LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning
by: Yu, Han, et al.
Published: (2022)

Temporal Gaussian Copula For Clinical Multivariate Time Series Data Imputation
by: Su, Ye, et al.
Published: (2025)

Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data
by: Kelleher, John D., et al.
Published: (2025)

One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data
by: Luetto, Simone, et al.
Published: (2023)

FactoryNet: A Large-Scale Dataset toward Industrial Time-Series Foundation Models
by: Othman, Karim, et al.
Published: (2026)

ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
by: Xie, Zhe, et al.
Published: (2024)

TimeWak: Temporal Chained-Hashing Watermark for Time Series Data
by: Soi, Zhi Wen, et al.
Published: (2025)

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection
by: Leppich, Robert, et al.
Published: (2025)

Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series
by: Schlegel, Udo, et al.
Published: (2024)

TimeSqueeze: Dynamic Patching for Efficient Time Series Forecasting
by: Ankireddy, Sravan Kumar, et al.
Published: (2026)

PFML: Self-Supervised Learning of Time-Series Data Without Representation Collapse
by: Vaaras, Einari, et al.
Published: (2024)

Stable Neural Stochastic Differential Equations in Analyzing Irregular Time Series Data
by: Oh, YongKyung, et al.
Published: (2024)

Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining
by: Yan, Xu, et al.
Published: (2024)

Review of Data-centric Time Series Analysis from Sample, Feature, and Period
by: Sun, Chenxi, et al.
Published: (2024)

Extending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation
by: Dobhal, Umang, et al.
Published: (2026)

Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
by: Yang, Yuxuan, et al.
Published: (2025)

Optimizing the Training Diet: Data Mixture Search for Robust Time Series Forecasting
by: Pennino, Federico, et al.
Published: (2025)

Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoning
by: Zhou, Jiahui, et al.
Published: (2026)

Interpretable Pre-Trained Transformers for Heart Time-Series Data
by: Davies, Harry J., et al.
Published: (2024)