Saved in:
| Main Authors: | Baer, Gregor, Grau, Isel, Zhang, Chao, Van Gorp, Pieter |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.11790 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Class-Dependent Perturbation Effects in Evaluating Time Series Attributions
by: Baer, Gregor, et al.
Published: (2025)
by: Baer, Gregor, et al.
Published: (2025)
Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding
by: Baer, Gregor, et al.
Published: (2026)
by: Baer, Gregor, et al.
Published: (2026)
xaitimesynth: A Python Package for Evaluating Attribution Methods for Time Series with Synthetic Ground Truth
by: Baer, Gregor
Published: (2026)
by: Baer, Gregor
Published: (2026)
Hypothesis Class Determines Explanation: Why Accurate Models Disagree on Feature Attribution
by: B, Thackshanaramana
Published: (2026)
by: B, Thackshanaramana
Published: (2026)
Why Do Transformers Fail to Forecast Time Series In-Context?
by: Zhou, Yufa, et al.
Published: (2025)
by: Zhou, Yufa, et al.
Published: (2025)
Why Do Time Series Models Need Long Context Windows?
by: Butera, Luca, et al.
Published: (2026)
by: Butera, Luca, et al.
Published: (2026)
An Automated Approach to Collecting and Labeling Time Series Data for Event Detection Using Elastic Node Hardware
by: Ling, Tianheng, et al.
Published: (2024)
by: Ling, Tianheng, et al.
Published: (2024)
Class-incremental Learning for Time Series: Benchmark and Evaluation
by: Qiao, Zhongzheng, et al.
Published: (2024)
by: Qiao, Zhongzheng, et al.
Published: (2024)
Synthetic Series-Symbol Data Generation for Time Series Foundation Models
by: Wang, Wenxuan, et al.
Published: (2025)
by: Wang, Wenxuan, et al.
Published: (2025)
TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data
by: Taga, Ege Onur, et al.
Published: (2025)
by: Taga, Ege Onur, et al.
Published: (2025)
STEB: In Search of the Best Evaluation Approach for Synthetic Time Series
by: Stenger, Michael, et al.
Published: (2025)
by: Stenger, Michael, et al.
Published: (2025)
On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data
by: Ling, Tianheng, et al.
Published: (2023)
by: Ling, Tianheng, et al.
Published: (2023)
Enabling Granular Subgroup Level Model Evaluations by Generating Synthetic Medical Time Series
by: Ibrahim, Mahmoud, et al.
Published: (2025)
by: Ibrahim, Mahmoud, et al.
Published: (2025)
Warping and Matching Subsequences Between Time Series
by: Lin, Simiao, et al.
Published: (2025)
by: Lin, Simiao, et al.
Published: (2025)
Causal SHAP: Feature Attribution with Dependency Awareness through Causal Discovery
by: Ng, Woon Yee, et al.
Published: (2025)
by: Ng, Woon Yee, et al.
Published: (2025)
On-device AI: Quantization-aware Training of Transformers in Time-Series
by: Ling, Tianheng, et al.
Published: (2024)
by: Ling, Tianheng, et al.
Published: (2024)
One Transformer for All Time Series: Representing and Training with Time-Dependent Heterogeneous Tabular Data
by: Luetto, Simone, et al.
Published: (2023)
by: Luetto, Simone, et al.
Published: (2023)
CauKer: Classification Time Series Foundation Models Can Be Pretrained on Synthetic Data
by: Xie, Shifeng, et al.
Published: (2025)
by: Xie, Shifeng, et al.
Published: (2025)
Explaining Time Series Classification Predictions via Causal Attributions
by: Alcaraz, Juan Miguel Lopez, et al.
Published: (2024)
by: Alcaraz, Juan Miguel Lopez, et al.
Published: (2024)
A Polynomial-Time Axiomatic Alternative to SHAP for Feature Attribution
by: Hiraki, Kazuhiro, et al.
Published: (2026)
by: Hiraki, Kazuhiro, et al.
Published: (2026)
A Dual-Perspective Approach to Evaluating Feature Attribution Methods
by: Li, Yawei, et al.
Published: (2023)
by: Li, Yawei, et al.
Published: (2023)
Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters
by: Cazaux, Hugo, et al.
Published: (2026)
by: Cazaux, Hugo, et al.
Published: (2026)
Review of Data-centric Time Series Analysis from Sample, Feature, and Period
by: Sun, Chenxi, et al.
Published: (2024)
by: Sun, Chenxi, et al.
Published: (2024)
SEED: Spectral Entropy-Guided Evaluation of SpatialTemporal Dependencies for Multivariate Time Series Forecasting
by: Xiong, Feng, et al.
Published: (2025)
by: Xiong, Feng, et al.
Published: (2025)
MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies
by: Feofanov, Vasilii, et al.
Published: (2026)
by: Feofanov, Vasilii, et al.
Published: (2026)
Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
by: Vapsi, Annita, et al.
Published: (2026)
by: Vapsi, Annita, et al.
Published: (2026)
Leaning Time-Varying Instruments for Identifying Causal Effects in Time-Series Data
by: Cheng, Debo, et al.
Published: (2024)
by: Cheng, Debo, et al.
Published: (2024)
Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data
by: Rewicki, Ferdinand, et al.
Published: (2025)
by: Rewicki, Ferdinand, et al.
Published: (2025)
IRNN: Innovation-driven Recurrent Neural Network for Time-Series Data Modeling and Prediction
by: Zhou, Yifan, et al.
Published: (2025)
by: Zhou, Yifan, et al.
Published: (2025)
UniTST: Effectively Modeling Inter-Series and Intra-Series Dependencies for Multivariate Time Series Forecasting
by: Liu, Juncheng, et al.
Published: (2024)
by: Liu, Juncheng, et al.
Published: (2024)
DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
by: Blöbaum, Patrick, et al.
Published: (2022)
by: Blöbaum, Patrick, et al.
Published: (2022)
Protect and Extend -- Using GANs for Synthetic Data Generation of Time-Series Medical Records
by: Ashrafi, Navid, et al.
Published: (2024)
by: Ashrafi, Navid, et al.
Published: (2024)
FreqLens: Interpretable Frequency Attribution for Time Series Forecasting
by: Chen, Chi-Sheng, et al.
Published: (2026)
by: Chen, Chi-Sheng, et al.
Published: (2026)
Impossibility Theorems for Feature Attribution
by: Bilodeau, Blair, et al.
Published: (2022)
by: Bilodeau, Blair, et al.
Published: (2022)
Time-Series Contrastive Learning against False Negatives and Class Imbalance
by: Jin, Xiyuan, et al.
Published: (2023)
by: Jin, Xiyuan, et al.
Published: (2023)
Synthetic Tabular Data Generation for Class Imbalance and Fairness: A Comparative Study
by: Panagiotou, Emmanouil, et al.
Published: (2024)
by: Panagiotou, Emmanouil, et al.
Published: (2024)
Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluation
by: Santos, Moisés, et al.
Published: (2023)
by: Santos, Moisés, et al.
Published: (2023)
CAFO: Feature-Centric Explanation on Time Series Classification
by: Kim, Jaeho, et al.
Published: (2024)
by: Kim, Jaeho, et al.
Published: (2024)
Backpropagation through Time Algorithm for Training Recurrent Neural Networks using Variable Length Instances
by: Isel Grau
Published: (2013)
by: Isel Grau
Published: (2013)
Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT
by: Ling, Tianheng, et al.
Published: (2024)
by: Ling, Tianheng, et al.
Published: (2024)
Similar Items
-
Class-Dependent Perturbation Effects in Evaluating Time Series Attributions
by: Baer, Gregor, et al.
Published: (2025) -
Does Explanation Correctness Matter? Linking Computational XAI Evaluation to Human Understanding
by: Baer, Gregor, et al.
Published: (2026) -
xaitimesynth: A Python Package for Evaluating Attribution Methods for Time Series with Synthetic Ground Truth
by: Baer, Gregor
Published: (2026) -
Hypothesis Class Determines Explanation: Why Accurate Models Disagree on Feature Attribution
by: B, Thackshanaramana
Published: (2026) -
Why Do Transformers Fail to Forecast Time Series In-Context?
by: Zhou, Yufa, et al.
Published: (2025)