Image de couverture de livre

Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Yan, Xu, Jiang, Yaoting, Liu, Wenyi, Yi, Didi, Wei, Jianjun
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

EVIL: Evolving Interpretable Algorithms for Zero-Shot Inference on Event Sequences and Time Series with LLMs
par: Berghaus, David
Publié: (2026)

Mechanistic Interpretability for Transformer-based Time Series Classification
par: Kalnāre, Matīss, et autres
Publié: (2025)

Interpretable Pre-Trained Transformers for Heart Time-Series Data
par: Davies, Harry J., et autres
Publié: (2024)

Matrix Profile for Anomaly Detection on Multidimensional Time Series
par: Yeh, Chin-Chia Michael, et autres
Publié: (2024)

EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions
par: Chakraborty, Sanjay, et autres
Publié: (2024)

Diffusion-TS: Interpretable Diffusion for General Time Series Generation
par: Yuan, Xinyu, et autres
Publié: (2024)

Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams
par: Nakamura, Kota, et autres
Publié: (2026)

ST-Tree with Interpretability for Multivariate Time Series Classification
par: Du, Mingsen, et autres
Publié: (2024)

Interpreting Outliers in Time Series Data through Decoding Autoencoder
par: Knab, Patrick, et autres
Publié: (2024)

TemporalBench: A Benchmark for Evaluating LLM-Based Agents on Contextual and Event-Informed Time Series Tasks
par: Weng, Muyan, et autres
Publié: (2026)

EventTSF: Event-Aware Non-Stationary Time Series Forecasting
par: Ge, Yunfeng, et autres
Publié: (2025)

TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Model
par: Cao, Defu, et autres
Publié: (2024)

SEDformer: Event-Synchronous Spiking Transformers for Irregular Telemetry Time Series Forecasting
par: Zhou, Ziyu, et autres
Publié: (2026)

EVEREST: An Evidential, Tail-Aware Transformer for Rare-Event Time-Series Forecasting
par: Zilinskas, Antanas, et autres
Publié: (2026)

Correlating Time Series with Interpretable Convolutional Kernels
par: Chen, Xinyu, et autres
Publié: (2024)

Estimating Mutual Information between Time Series and Temporal Event Sequences Across Diverse Analysis Tasks
par: Hu, Haoji, et autres
Publié: (2026)

Diffusion Transformers for Tabular Data Time Series Generation
par: Garuti, Fabrizio, et autres
Publié: (2025)

XTSFormer: Cross-Temporal-Scale Transformer for Irregular-Time Event Prediction in Clinical Applications
par: Xiao, Tingsong, et autres
Publié: (2024)

Semiconductor Industry Trend Prediction with Event Intervention Based on LSTM Model in Sentiment-Enhanced Time Series Data
par: Yen, Wei-hsiang, et autres
Publié: (2025)

HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
par: Wang, Tan, et autres
Publié: (2025)

Explanation Space: A New Perspective into Time Series Interpretability
par: Rezaei, Shahbaz, et autres
Publié: (2024)

Enhancing Transformer-based models for Long Sequence Time Series Forecasting via Structured Matrix
par: Zhang, Zhicheng, et autres
Publié: (2024)

When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference
par: Ye, Wen, et autres
Publié: (2025)

Pretext Training Algorithms for Event Sequence Data
par: Wang, Yimu, et autres
Publié: (2024)

Capture Timing-Attention of Events in Clinical Time Series
par: Li, Jia, et autres
Publié: (2026)

Kolmogorov-Arnold Networks for Time Series: Bridging Predictive Power and Interpretability
par: Xu, Kunpeng, et autres
Publié: (2024)

Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting
par: Xu, Jingjing, et autres
Publié: (2024)

Generative Models for Synthetic Data: Transforming Data Mining in the GenAI Era
par: Li, Dawei, et autres
Publié: (2025)

VARMA-Enhanced Transformer for Time Series Forecasting
par: Song, Jiajun, et autres
Publié: (2025)

CITRAS: Covariate-Informed Transformer for Time Series Forecasting
par: Yamaguchi, Yosuke, et autres
Publié: (2025)

Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings
par: Sakhinana, Sagar Srinivas, et autres
Publié: (2024)

Interpretable Time Series Autoregression for Periodicity Quantification
par: Chen, Xinyu, et autres
Publié: (2025)

Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
par: Kan, Ziwen, et autres
Publié: (2024)

Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality
par: Liu, Ziqi, et autres
Publié: (2025)

Transformers and Their Roles as Time Series Foundation Models
par: Wu, Dennis, et autres
Publié: (2025)

Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates
par: Yang, Linxiao, et autres
Publié: (2026)

TimeCAP: Learning to Contextualize, Augment, and Predict Time Series Events with Large Language Model Agents
par: Lee, Geon, et autres
Publié: (2025)

CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
par: Lu, Jiecheng, et autres
Publié: (2024)

A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
par: Yi, Kun, et autres
Publié: (2023)

Evaluating Simplification Algorithms for Interpretability of Time Series Classification
par: Håvardstun, Brigt, et autres
Publié: (2025)