Forecasting

Latest papers

9 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

What Is the Influence of Task Order in Continual Learning? An Empirical Study and Order-Aware Evaluation for Motion Forecasting
Nov 3, 2026Zirui Li, Yunlong Lin, Haohan Yang +2Forecasting Continual Learning
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798 · School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China, and also with the School of Computation, Information and Technology, Technical University of Munich, 85748 Garching, Germany · Department of Civil and Environmental Engineering, Samueli School of Engineering, University of California, Los Angeles, CA 90095 USA +1
MoRE-GS: Motion-Regime Experts for Future Gaussian Splatting
Oct 30, 2026Shima Salehi, Joshua PeeplesForecasting Dynamic Novel View Synthesis
Department of Electrical and Computer Engineering, Texas A&M University, College Station, Texas, USA
Patient, Place, Prior (P $^3$ ): What Counts as Personalization in Medical World Models?
Oct 6, 2026Xingrui Gu, Hanxue Gu, Yuxiang Zhang +1Forecasting Longitudinal Medical Image Analysis
University of California, Berkeley · University of California, San Francisco · Southern Methodist University
Target alignment, dilution and forecast selection when cross-sectional forecasts share a common target
Sep 22, 2026Masoud SoleimaniModel Selection Financial Forecasting
Department of Information Engineering, University of Pisa, Pisa, Italy
Asymmetric Peak-Aware Loss for Peak-Critical Time Series Forecasting
Jul 16, 2026Theivaprakasham Hari, Yanan Xin, Winnie Daamen +2Multivariate Time Series Forecasting Long-Term Time Series Forecasting
Department of Transport and Planning, Faculty of Civil Engineering and Geosciences Delft University of Technology, Delft, The Netherlands · Mobility Innovation Centre Delft University of Technology, Delft, The Netherlands
Accelerated Spatio-Temporal Gaussian Rendering via Kinematic and Semantic Priors
May 25, 2026Denis Gridusov, Maxim Popov, Sergey Kolyubin3D Gaussian Splatting 4D Reconstruction
ITMO University, St. Petersburg, Russia
4DLidarOpen: An Open 4D FMCW Lidar Dataset for Motion-Aware Autonomous Driving
May 18, 2026Kane Qian, Xin Zhao, Yining Shi +7Autonomous Driving Datasets Autonomous Driving
1Tsinghua University. · 2Hesai Technology Co., Ltd.
Analytica: Soft Propositional Reasoning for Robust and Scalable LLM-Driven Analysis
Apr 24, 2026Junyan Cheng, Kyle Richardson, Peter ChinLLM Agent Orchestration LLM Agents
Dartmouth Collegeα · Allen Institute for AIβ
Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models
Jul 28, 2024Jifeng Wang, Kaouther Messaoud, Yuejiang Liu +2Forecasting Parameter-Efficient Fine-Tuning
University of Bonn, Bonn, Germany · VITA Laboratory, EPFL, Lausanne, Switzerland · Stanford University, Stanford, USA

Nov 3, 2026J/K move · Enter open · S save

What Is the Influence of Task Order in Continual Learning? An Empirical Study and Order-Aware Evaluation for Motion Forecasting

Zirui Li, Yunlong Lin, Haohan Yang, Xiaocong Zhao+1

School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798 · School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China, and also with the School of Computation, Information and Technology, Technical University of Munich, 85748 Garching, Germany · Department of Civil and Environmental Engineering, Samueli School of Engineering, University of California, Los Angeles, CA 90095 USA+1

Forecasting Continual Learning Incremental Learning

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Aggregate scores for continual learning algorithms rest on a distribution over task orders that is rarely stated. We formulate algorithm comparison as an expectation over a declared order distribution, which separates training variation from order variation, and apply it to motion forecasting with four road-layout tasks from the INTERACTION dataset, all 24 task orders, four algorithms, and three training replicates (288 runs). Task order accounts for 65% to 91% of the variation in current-task error and backward transfer, and 90% to 99% of this order effect comes from the identity of the starting task: the smallest task cannot be learned from a cold start. Beyond the first stage, the order effect is comparable to training variability. In most orders, the leading algorithm in current-task error and backward transfer is within one replicate standard deviation of the runner-up. Backward transfer depends on the order mainly through its reference term, which weights the first task most heavily, so relative improvement and final error identify different leading algorithms for displacement error. A transition-direction descriptor computed from task statistics tracks the position of the smallest task, and weighting orders by it leaves the algorithm ranking unchanged. These results call for benchmarks that declare their order distribution and report order-resolved, starting-task-resolved, warm-start, and final errors alongside aggregate scores. The code and data are available here.