Latest Time Series Research Papers

The newest Time Series papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Time Series so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting
  Zesheng Liu, Maryam Rahnemoonfar · arXiv · Jun 9, 2026

  In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes. COGENT encodes a finite history of system states and associated forc…

• OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
  Abhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026

  Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…

• Zero Touch Predictive Orchestration: Automating Time-Series Models for the Cloud-Edge Continuum
  Abd Elghani Meliani, Arora Sagar, Adlen Ksentini, Raymond Knopp · arXiv · Jun 8, 2026

  The Cloud-Edge Continuum (CEC) enables latency-critical applications by distributing resources to the far edge, but its extreme volatility makes proactive Zero Touch Management via time-series forecasting essential. However, orchestrators f…

• Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
  Lorenzo Longarini, Alessandro Rongoni, Simone Silenzi, Emanuele Frontoni et al. · arXiv · Jun 5, 2026

  At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of standard supervised forecasters. This cold-start setting is addressed with a zero-shot pip…

• Proper Scoring Rules for Right-Censored Survival Data
  Jef Jonkers, Glenn Van Wallendael, Luc Duchateau, Sofie Van Hoecke · arXiv · Jun 4, 2026

  Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts. However, in the presence of right censoring, the event time is only partially observed, rendering conventional scoring rul…

• ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
  Zhensheng Wang, Xiaole Liu, Wenmian Yang, Kun Zhou et al. · arXiv · Jun 1, 2026

  The rapid development of LLMs has significantly advanced tabular question answering, but most systems cannot perform future-oriented numerical prediction. To address this gap, we introduce a novel task, Open-Domain Tabular Question Answerin…

• Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series
  Hanyang Jiang, Rina Foygel Barber, Ashwin Pananjady, Yao Xie · arXiv · May 28, 2026

  Conformal prediction methods enjoy strong theoretical and empirical predictive inference performance, provided the data is exchangeable, and predictors are trained in a memoryless fashion. However, these assumptions and constraints are impr…

• Falcon-X: A Time Series Foundation Model for Heterogeneous Multivariate Modeling
  Yiding Liu, Yifan Hu, Hongjie Xia, Peiyuan Liu et al. · arXiv · May 26, 2026

  Time series foundation models (TSFMs) are transforming the forecasting paradigm through large-scale cross-domain pretraining. However, most existing TSFMs remain univariate, and recent efforts to enable cross-variate modeling still operate …

• Transfer Learning using 66 Diseases for Disease Forecasting Applications
  Lauren J Beesley, Alexander C Murph, Dave Osthus, Lauren A Castro · arXiv · May 26, 2026

  Disease forecasting models typically rely on a single data stream, making models brittle when histories are short or noisy. Recent top-performing models have shown that synthesizing multiple reporting systems for the same disease improves p…

• FutureSim: Replaying World Events to Evaluate Adaptive Agents
  Shashwat Goel, Nikhil Chandak, Arvindh Arun, Ameya Prabhu et al. · arXiv · May 14, 2026

  AI agents are being increasingly deployed in dynamic, open-ended environments that require adapting to new information as it arrives. To efficiently measure this capability for realistic use-cases, we propose building grounded simulations t…

• Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment
  Sayantan Kumar, Shahriar Noroozizadeh, Juyong Kim, Jeremy C. Weiss · arXiv · May 14, 2026

  Reconstructing precise clinical timelines is essential for modeling patient trajectories and forecasting risk in complex, heterogeneous conditions like sepsis. While unstructured clinical narratives offer semantically rich and contextually …

• V4FinBench: Benchmarking Tabular Foundation Models, LLMs, and Standard Methods on Corporate Bankruptcy Prediction
  Marcin Kostrzewa, Sebastian Tomczak, Roman Furman, Anna Poberezhna et al. · arXiv · May 11, 2026

  Corporate bankruptcy prediction is a high-stakes financial task characterized by severe class imbalance and multi-horizon forecasting demands. Public datasets supporting it remain scarce and small: widely used free benchmarks contain betwee…

• STEPS: A Temporal Smooth Error Propagation Solver on the Manifolds for Test-Time Adaptation in Time Series Forecasting
  Jiaqi Liu, Yifan Ouyang, Zhifei Song, Sim Kuan Goh et al. · arXiv · May 8, 2026

  Test-Time Adaptation (TTA) aims to improve time series forecasting under distribution shifts by using limited observations revealed during inference. However, forecasting TTA must operate in a source-free online setting, where the adaptatio…

• Superposition Is Not Necessary: A Mechanistic Interpretability Analysis of Transformer Representations for Time Series Forecasting
  Alper Yıldırım · arXiv · May 6, 2026

  Transformer architectures have been widely adopted for time series forecasting, yet whether the representational mechanisms that make them powerful in NLP actually engage on time series data remains unexplored. The persistent competitivenes…

• Human-AI Co-Mentorship in Project-Based Learning: A Case Study in Financial Forecasting
  Freyaa Chawla, Ahan Chawla, Rishi Singh, Joe Germino et al. · arXiv · May 6, 2026

  This paper reflects on a AI research project carried out by a team of high-school and early-undergraduate students under the mentorship of graduate researchers and ably assisted by AI tools. We share our experience in not only on the learni…

• Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models
  Matthias Hertel, Alexandra Nikoltchovska, Sebastian Pütz, Ralf Mikut et al. · arXiv · Apr 30, 2026

  Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in energy systems. However, applications in critical infrastructure like power grids require …

• Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
  Gongbo Zhang, Wen Wang, Ye Tian, Li Yuan · arXiv · Apr 29, 2026

  Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference…

• Electricity price forecasting across Norway's five bidding zones in the post-crisis era
  My Thi Diem Phan, Trung Tuyen Truong, Hoai Phuong Ha, Dat Thanh Nguyen · arXiv · Apr 29, 2026

  Norway's electricity market is heavily dominated by hydropower, but the 2021--2022 energy crisis and stronger integration with Continental Europe have fundamentally altered price formation, reducing the reliability of forecasting models cal…

• Inferring bifurcation diagrams of two distinct chaotic systems by a single machine
  Jianmin Guo, Yao Du, Yizhen Yu, Yong Zou et al. · arXiv · Apr 29, 2026

  We propose a dual-channel reservoir-computing scheme for inferring the dynamics of two distinct chaotic systems with a single machine. By augmenting a standard reservoir with a system-label channel and a parameter-control channel, the machi…

• Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting
  Max Kleinebrahm, Jonathan Berrisch, Philipp Eiser, Wolf Fichtner et al. · arXiv · Apr 27, 2026

  Energy forecasting research faces a persistent comparability gap that makes it difficult to measure consistent progress over time. Reported accuracy gains are often not directly comparable because models are evaluated under study-specific d…

• CLVAE: A Variational Autoencoder for Long-Term Customer Revenue Forecasting
  Jeffrey Näf, Riana Valera Mbelson, Markus Meierer · arXiv · Apr 24, 2026

  Predicting customers' long-term revenue from sparse and irregular transaction data is central to marketing resource allocation in non-contractual settings, yet existing approaches face a trade-off. Traditional probabilistic customer base mo…

• Explanation of Dynamic Physical Field Predictions using WassersteinGrad: Application to Autoregressive Weather Forecasting
  Younes Essafouri, Laure Raynaud, Luciano Drozda, Laurent Risser · arXiv · Apr 24, 2026

  As the demand to integrate Artificial Intelligence into high-stakes environments continues to grow, explaining the reasoning behind neural-network predictions has shifted from a theoretical curiosity to a strict operational requirement. Our…

• Global Offshore Wind Infrastructure: Deployment and Operational Dynamics from Dense Sentinel-1 Time Series
  Thorsten Hoeser, Felix Bachofer, Claudia Kuenzer · arXiv · Apr 22, 2026

  The offshore wind energy sector is expanding rapidly, increasing the need for independent, high-temporal-resolution monitoring of infrastructure deployment and operation at global scale. While Earth Observation based offshore wind infrastru…

• VLA Foundry: A Unified Framework for Training Vision-Language-Action Models
  Jean Mercat, Sedrick Keh, Kushal Arora, Isabella Huang et al. · arXiv · Apr 21, 2026

  We present VLA Foundry, an open-source framework that unifies LLM, VLM, and VLA training in a single codebase. Most open-source VLA efforts specialize on the action training stage, often stitching together incompatible pretraining pipelines…

• IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem
  Aniruddha Adiga, Jingyuan Chou, Anshul Chiranth, Bryan Lewis et al. · arXiv · Apr 20, 2026

  Epidemic forecasting has become an integral part of real-time infectious disease outbreak response. While collaborative ensembles composed of statistical and machine learning models have become the norm for real-time forecasting, standardiz…

• Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting
  Worachit Amnuaypongsa, Yotsapat Suparanonrat, Pana Wanitchollakit, Jitkomut Songsiri · arXiv · Apr 20, 2026

  This paper proposes a multi-step probabilistic forecasting framework using a single neural-network based model to generate simultaneous point and interval forecasts. Our approach ensures non-crossing prediction intervals (PIs) through a mod…

• A Hybrid Neural Network Approach to Controllability in Caputo Fractional Neutral Integro-Differential Systems for Cryptocurrency Forecasting
  Prabakaran Raghavendran, Yamini Parthiban · Fractal and Fractional · Apr 18, 2026

  This research paper demonstrates how to manage Caputo fractional neutral integro-differential equations which include both integral and nonlinear elements through a unified framework that models dynamic systems with memory-based dynamics. T…

• Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction
  Hannah Guan, Soukayna Mouatadid, Paulo Orenstein, Judah Cohen et al. · arXiv · Apr 17, 2026

  Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such forecasts enjoy unprecedented accuracy out to two weeks thanks to steady advances in physic…

• Univariate Channel Fusion for Multivariate Time Series Classification
  Fernando Moro, Vinicius M. A. Souza · arXiv · Apr 17, 2026

  Multivariate time series classification (MTSC) plays a crucial role in various domains, including biomedical signal analysis and motion monitoring. However, existing approaches, particularly deep learning models, often require high computat…

• MambaSL: Exploring Single-Layer Mamba for Time Series Classification
  Yoo-Min Jung, Leekyung Kim · arXiv · Apr 16, 2026

  Despite recent advances in state space models (SSMs) such as Mamba across various sequence domains, research on their standalone capacity for time series classification (TSC) has remained limited. We propose MambaSL, a framework that minima…