Cover Image

Predictability-Aware Compression and Decompression Framework for Multichannel Time Series Data with Latent Seasonality

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Liu, Ziqi, Zeng, Pei, Ding, Yi
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Decomposition Modeling Framework for Seasonal Time-Series Forecasting
by: Pang, Yining, et al.
Published: (2024)

EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models
by: Zhou, Xinxing, et al.
Published: (2026)

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

ST-MTM: Masked Time Series Modeling with Seasonal-Trend Decomposition for Time Series Forecasting
by: Seo, Hyunwoo, et al.
Published: (2025)

Relevance-Aware Thresholding in Online Conformal Prediction for Time Series
by: Dupuy, Théo, et al.
Published: (2025)

SageFormer: Series-Aware Framework for Long-term Multivariate Time Series Forecasting
by: Zhang, Zhenwei, et al.
Published: (2023)

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

Can LLMs Compress (and Decompress)? Evaluating Code Understanding and Execution via Invertibility
by: Maveli, Nickil, et al.
Published: (2026)

DeepSelective: Interpretable Prognosis Prediction via Feature Selection and Compression in EHR Data
by: Zhang, Ruochi, et al.
Published: (2025)

Latent Laplace Diffusion for Irregular Multivariate Time Series
by: You, Zinuo, et al.
Published: (2026)

Beyond Model Ranking: Predictability-Aligned Evaluation for Time Series Forecasting
by: Feng, Wanjin, et al.
Published: (2025)

Impute With Confidence: A Framework for Uncertainty Aware Multivariate Time Series Imputation
by: Weatherhead, Addison, et al.
Published: (2025)

Predicting Time Series of Networked Dynamical Systems without Knowing Topology
by: Ding, Yanna, et al.
Published: (2024)

A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting
by: Park, Young-Jin, et al.
Published: (2024)

HRS: Hybrid Representation Framework with Scheduling Awareness for Time Series Forecasting in Crowdsourced Cloud-Edge Platforms
by: Zhang, Tiancheng, et al.
Published: (2025)

Lossless Compression: A New Benchmark for Time Series Model Evaluation
by: Wan, Meng, et al.
Published: (2025)

LightSAE: Parameter-Efficient and Heterogeneity-Aware Embedding for IoT Multivariate Time Series Forecasting
by: Ren, Yi, et al.
Published: (2025)

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

Hierarchical Time Series Forecasting Via Latent Mean Encoding
by: Salatiello, Alessandro, et al.
Published: (2025)

Dual-Prototype Disentanglement: A Context-Aware Enhancement Framework for Time Series Forecasting
by: Yang, Haonan, et al.
Published: (2026)

xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition
by: Stitsyuk, Artyom, et al.
Published: (2024)

SWAN: Sparse Winnowed Attention for Reduced Inference Memory via Decompression-Free KV-Cache Compression
by: S, Santhosh G, et al.
Published: (2025)

Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting
by: Zhu, Jiawen, et al.
Published: (2026)

ReTAMamba: Reliability-Aware Temporal Aggregation with Mamba for Irregular Clinical Time Series Prediction
by: Kim, Jinwoong, et al.
Published: (2026)

TailedTS: Benchmark Dataset for Heavy-Tailed Time Series Prediction and Periodicity Quantification
by: Chen, Xinyu, et al.
Published: (2026)

Population Aware Diffusion for Time Series Generation
by: Li, Yang, et al.
Published: (2025)

Reasoning-Aware Training for Time Series Forecasting
by: Ahamed, Md Atik, et al.
Published: (2026)

Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing
by: Jalali, Amin, et al.
Published: (2025)

Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility
by: Yu, Annan, et al.
Published: (2025)

TimeCapsule: Solving the Jigsaw Puzzle of Long-Term Time Series Forecasting with Compressed Predictive Representations
by: Lu, Yihang, et al.
Published: (2025)

Towards Stable and Structured Time Series Generation with Perturbation-Aware Flow Matching
by: Zhang, Jintao, et al.
Published: (2025)

A Hybrid PCA-PR-Seq2Seq-Adam-LSTM Framework for Time-Series Power Outage Prediction
by: Das, Subhabrata, et al.
Published: (2025)

TSI: A Multi-View Representation Learning Approach for Time Series Forecasting
by: Gao, Wentao, et al.
Published: (2024)

IndexNet: Timestamp and Variable-Aware Modeling for Time Series Forecasting
by: Wu, Beiliang, et al.
Published: (2025)

pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
by: Wang, Zhijin, et al.
Published: (2025)

TimeMKG: Knowledge-Infused Causal Reasoning for Multivariate Time Series Modeling
by: Sun, Yifei, et al.
Published: (2025)

Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction
by: Ngo, Vuong M., et al.
Published: (2025)

IRNN: Innovation-driven Recurrent Neural Network for Time-Series Data Modeling and Prediction
by: Zhou, Yifan, 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)

Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecasting
by: Hu, Yifan, et al.
Published: (2025)