Time Series

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Period ending 2026-09-21

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256 papers

Latest in Time Series

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Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining
Jun 6, 2026Aaryan Nagpal, Debdeep Sanyal, Murari Mandal +2Time Series Foundation Models Model Pretraining
Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors
Jun 5, 2026Tao Chen, Yexu Zhou, Zhi Gong +9Time Series Tabular Prior-Data Fitted Network
CF-JEPA: Mask-free forward prediction with asymmetric encoder utilization for time-series representation learning
Jun 5, 2026Jaehoon Lee, Sunghyun SimJoint-Embedding Predictive Architectures Sphere-Jepa
TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models
Jun 4, 2026Ziwen Kan, Yishuo Chen, Kecheng Li +7Multimodal Foundation Model Time Series Foundation Models
TS-ICL: A Flexible Time-Indexed Foundation Model for Time Series via In-Context Learning
Jun 4, 2026Etienne Le Naour, Tahar Nabil, Adrien PetraliaTime Series Foundation Models Time Series
Harnessing Generalist Agents for Contextualized Time Series
Jun 3, 2026Zihao Li, Kaifeng Jin, Yuanchen Bei +8Time Series Temporal Dynamics
Generating Financial Time Series by Matching Random Convolutional Features
Jun 3, 2026Konrad J. Mueller, Nikita Zozoulenko, Ben Wood +2Synthetic Time Series Time Series
REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting
Jun 3, 2026Moulik Gupta, Dhruv Kumar, Murari Mandal +1Synthetic Time Series Multivariate Time Series Forecasting
Signed Dual Attention: Capturing Signed Dependencies in Time Series Forecasting
Jun 3, 2026Balthazar Courvoisier, Tristan CazenaveTransformer Attention Time Series
ADAPTOOD: Uncertainty-Aware Fine-Tuning for Out-of-Distribution ECG Time Series Models
Jun 2, 2026Sotirios Vavaroutas, Yu Yvonne Wu, Ali Etemad +1Out-Of-Distribution Distribution Shifts
Stationarity-Aware Retrieval-Augmented Time Series Forecasting
Jun 2, 2026Shiqiao Zhou, Holger Schöner, Zipeng Wu +3Time Series Autoregressive Model
AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE
Jun 2, 2026Tao Xie, Zexi Tan, Haoyi Xiao +5Time-Series Classification Time Series
TSQAgent: Rating Time Series Data Quality via Dedicated Agentic Reasoning
Jun 2, 2026Shunyu Wu, Dan Li, Haozheng Ye +6Data Quality Agentic Reasoning
ProbRes: Volatility Learning for Probabilistic Time-Series Forecasting
Jun 1, 2026Tingting Wang, Yunyi Zhang, Benyou WangMultivariate Forecasting Heteroskedasticity
Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate
Jun 1, 2026Marc Pinet, Julien Cumin, Samuel Berlemont +1Time-Series Anomaly Detection Anomaly
Why Do Time Series Models Need Long Context Windows?
Jun 1, 2026Luca Butera, Giovanni De Felice, Andrea Cini +1Time Series Local Windows
Estimating Mutual Information between Time Series and Temporal Event Sequences Across Diverse Analysis Tasks
Jun 1, 2026Haoji Hu, Huaqing Mao, Yijun Lin +4Temporal Dependence Time Series
TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning
May 31, 2026Yaxuan Kong, Qingren Yao, Yuqi Nie +7Agentic Benchmarks Time Series
Time Series as Language: A Universal Tokenizer for General-Purpose Time Series Foundation Models
May 31, 2026Yunhao Zhang, Ruiying Qi, Jiale Zheng +3Time Series Foundation Models Time Series
FAiT: Frequency-Aware Inverted Transformer for Multivariate Time Series Forecasting
May 31, 2026Peng He, Yao Liu, Yanglei Gan +3Multivariate Time Series Forecasting Time Series
ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection
May 31, 2026Uzair Khan, Luigi Capogrosso, Francesco Biondani +4Time-Series Anomaly Detection Anomaly Detection
Feature to Dynamics: Feature-space to Autoregression strategy for Zero-shot Time Series Forecasting
May 31, 2026Yifan Wu, Junjie Wu, Kai Wu +2Zero-Shot Forecasting Zero-Shot Inference
Learning Hyperspherical Time-Frequency Representations for Time-Series Out-of-Distribution Detection
May 29, 2026Willian T. Lunardi, Samridha Shrestha, Martin AndreoniOut-Of-Distribution Detection Time-Frequency
Generalizing Multi-Scale Time-Series Modeling with a Single Operator
May 29, 2026Cheonwoo Lee, Dooho Lee, Doyun Choi +1Time Series Multiscale
SRT: Super-Resolution for Time Series via Disentangled Rectified Flow
May 29, 2026Jufang Duan, Shenglong Xiao, Yuren ZhangSuper-Resolution Framework Time Series
STEP: Learning STructured Embeddings for Progressive Time Series
May 29, 2026Lucas Thil, Jesse Read, Rim Kaddah +1Time Series Next Activity Prediction
Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series
May 28, 2026Hanyang Jiang, Rina Foygel Barber, Ashwin Pananjady +1Conformal Prediction Multivariate Forecasting
Online Irregular Multivariate Time Series Forecasting via Uncertainty-Driven Dual-Expert Calibration
May 27, 2026Haonan Wen, Hanyang Chen, Songhe FengMultivariate Time Series Forecasting Time Series
Detecting Diffusion-Generated Time Series Under Generator Shift
May 27, 2026Zhi Wen Soi, Aditya Shankar, Gert Lek +4Synthetic Time Series Generators
Inpainting-Style Conditional Diffusion for Multivariable Time Series Forecasting
May 27, 2026Kourosh Kiani, S. M. MuyeenEnergy Forecasting Multivariate Time Series Forecasting
Identifying Explicit Parsimonious Piece-wise Polynomial Relationships in Industrial time-series: Application to manipulator robots
May 27, 2026Mazen Alamir, Sacha ClavelSystem Identification Robotic Arms
QuITE: Query-Based Irregular Time Series Embedding
May 27, 2026Junghoon LimTime Series Temporal Dynamics
Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
May 26, 2026Yiding Liu, Yifan Hu, Hongjie Xia +5Multivariate Time Series Forecasting Time Series Foundation Models
Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation
May 26, 2026Khayyam Nosrati, Martin Uray, Saverio Messineo +2Time-Series Anomaly Detection Federated Learning
High-Quality Synthetic Financial Time-Series using a GAN-Diffusion Framework
May 26, 2026Giuseppe Masi, Andrea Coletta, Novella BartoliniSynthetic Time Series Synthetic Data
Triangular-Reference Schrödinger Bridges for Time Series Generation
May 26, 2026Gabriele BocchiSchrödinger Bridges Synthetic Time Series
Unicorn: Scaling High-Dimensional Time Series Forecasting via Universal Correlation Modeling
May 26, 2026Haochen Yuan, Yichen Song, Yunbo Wang +1Multivariate Forecasting Time Series
Time Series Causal Discovery via Context-Conditioned and Causality-Augmented Pretraining
May 26, 2026Biao Ouyang, Tengxue Zhang, Zhihao Zhuang +3Causal Foundation Models Temporal Dependence
Distribution-Aware Conformal Prediction: A Framework for generating efficient prediction intervals for time series
May 26, 2026Daniel Schweizer, Peter Kuhn, Jayant Sharma +3Conformal Prediction Quantile Regression
FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis
May 26, 2026Peiyu Duan, Jiyao Wang, Nicha C. Dvornek +4Brain Magnetic Resonance Imaging Synthetic Time Series
AME-TS: Anchored Mixture-of-Experts for Time Series Forecasting
May 24, 2026Rui Wang, Renhao Xue, Ray Razi +2Time Series Foundation Models Time Series
TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
May 24, 2026Hongkai Li, Shifeng Xie, Lefei Shen +7Time Series Foundation Models Time Series
TS-Skill: A Benchmark for Evaluating Analytical Skills in Time-Series Question Answering
May 23, 2026Liying Han, Kang Yang, Oliver Wang +9Closed-Book Question Answering Time Series
Valid and Expressive Copulas for Irregular Multivariate Time Series
May 22, 2026Christian Klötergens, Tom Hanika, Lars Schmidt-Thieme +1Gaussian Copula Multivariate Time Series Forecasting
Parametric Prior Mapping Framework for Non-stationary Probabilistic Time Series Forecasting
May 22, 2026Jinglin Li, Jun Tan, QI Fang +1Non-Stationarity Time Series
Continuity and Ordinality Matter: Constraining Time Series Tokens for Effective Time Series Analysis with Large Language Models
May 22, 2026Musheng Li, Ziying Zhang, Cheng jin +1Time Series Sequence Modeling
PaP-NF: Probabilistic Long-Term Time Series Forecasting via Prefix-as-Prompt Reprogramming and Normalizing Flows
May 22, 2026Minju Kim, Youngbum HurMultivariate Forecasting Time Series
World Machine: Towards Generative World Modeling for Time-Series
May 21, 2026Elton Cardoso do Nascimento, Alexandre da Silva Simões, Esther Luna Colombini +2Generative Models Unified Transformer
ChronoVAE-HOPE: Beyond Attention -- A Next-Generation VAE Foundation Model for Specialized Time Series Classification
May 21, 2026José Alberto Rodríguez, Luis Balderas, Miguel Lastra +2Time-Series Classification Time Series
Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series
May 21, 2026Xianhao Song, Yuang Zhang, Yuqi She +2Time-Series Classification Time Series
Divide and Contrast: Learning Robust Temporal Features without Augmentation
May 20, 2026Abdul-Kazeem Shamba, Kerstin Bach, Gavin TaylorTemporal Deep Learning Framework Synthetic Data Augmentation
SURGE: An Event-Centric Social Media Sentiment Time Series Benchmark with Interaction Structure
May 20, 2026Chen Su, Pengsen Cheng, Yuanhe Tian +1Social Media Time Series
Reviving Error Correction in Modern Deep Time-Series Forecasting
May 20, 2026Minh Hoang Nguyen, Dai Do, Huu Hiep Nguyen +3Multivariate Forecasting Time Series
Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series
May 20, 2026Andi XuAlgorithm Auditing Time Series
LLM Pretraining Shapes a Generalizable Manifold: Insights into Cross-Modal Transfer to Time Series
May 19, 2026Alexis Roger, Prateek Humane, Zhenghan Tai +4Generative Pretrained Transformers Time Series
Toto 2.0: Time Series Forecasting Enters the Scaling Era
May 19, 2026Emaad Khwaja, Chris Lettieri, Gerald Woo +10Time Series Foundation Models Time Series
Latent Laplace Diffusion for Irregular Multivariate Time Series
May 19, 2026Zinuo You, Jin Zheng, John CartlidgeLong-Horizon Forecasting Time Series
Quantifying the Pre-training Dividend: Generative versus Latent Self-Supervised Learning for Time Series Foundation Models
May 19, 2026Noam Major, Kathy Razmadze, Yoli ShavitTime Series Foundation Models Self-Supervised
Chronicle: A Multimodal Foundation Model for Joint Language and Time Series Understanding
May 18, 2026Paul Quinlan, Jeremy Levasseur, Qingguo Li +1Time Series Foundation Models Time Series
KairosHope: A Next-Generation Time-Series Foundation Model for Specialized Classification via Dual-Memory Architecture
May 18, 2026Luis Balderas, José Alberto Rodríguez, Miguel Lastra +2Time Series Foundation Models Time Series

Jun 6, 2026 · cs.LGJ/K move · Enter open · S save

Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining

Aaryan Nagpal, Debdeep Sanyal, Murari Mandal, Dhruv Kumar+1

Birla AI Labs, Mumbai, India · KIIT, Bhubaneswar, India · BITS Pilani, Pilani, India

Choosing the wrong synthetic generator for time-series foundation model pretraining is costly: under identical training budgets, the best and worst generators produce up to a

2\times

gap in forecasting error, yet the field has no principled way to make this choice. The problem is compounded by the fact that generator rankings are not stable across architectures: across 11 generator families evaluated on Chronos-T5-Mini and Moirai-Small trained from scratch, we find that which generators are useful depends on the model architecture. Rather than solving the generator selection problem, we sidestep it: a simple equal-weight mixture of all generators matches or beats the best individual generator for both architectures, and composing this mixture with real data yields the strongest pretraining corpora overall. Synthetic pretraining is therefore a corpus composition problem, not a generator selection problem, and composition choices should be validated per model family rather than assumed to transfer.

Time Series Foundation Models Model Pretraining Time Series Text Corpora Mix Ranking

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