Forecasting Benchmarks

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21 papers in the last four weeks, up 250% on the four weeks before. 0.2% of all new papers.

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Benchmarking Time Series Generation Methods for Privacy-Preserving Forecasting
Aug 11, 2026Luis Amorim, Vitor Cerqueira, Moises Santos +2Synthetic Data Generation Time Series Forecasting
Faculdade de Engenharia da Universidade do Porto, Porto, Portugal · University of Coimbra, Coimbra, Portugal · Laboratory for Artificial Intelligence and Computer Science (LIACC), Portugal +3
Evaluating Generative Time-Series Models on Data with Point Masses
Aug 10, 2026Jian XuTime Series Forecasting Time Series Generation
RIKEN iTHEMS, Wako, Japan · RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan
Machine Learning and ARIMA Model Averaging for Adaptive Public Health Forecasting: Comparative Evaluation and an Ontario COVID-19 Case Study
Aug 7, 2026Yushu Zou, Ye Li, Johra Moosa +3Epidemic Forecasting Gradient-Boosted Decision Trees
Public Health Ontario, Toronto, Canada · Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Canada · Department of Laboratory Medicine and Pathobiology, University of Toronto, Canada +1
Enhancing Anomaly Resilience in Research Networks: A Large-Scale Forecasting Benchmark for Dynamic Security Baselining
Aug 6, 2026Mohammad Arafath Uddin Shariff, Byrav RamamurthyNetwork Intrusion Detection Forecasting Benchmarks
School of Computing University of Nebraska-Lincoln Lincoln, NE, USA
Short-term load forecasting under EU-AI Act Requirements in Safety-Critical Environments: Results from a 41-day live challenge on the aggregated German transmission-grid load
Aug 5, 2026Thomas Bartz-Beielstein, Inalbek Akiev, Lalo MohamadTime Series Forecasting Forecasting Benchmarks
THK-AI Research Cluster, Germany · TH Köln, Germany
TIDE: A Physically Diverse 3D Turbulence Benchmark Dataset for Advancing Scientific Machine Learning
Aug 4, 2026Yilong Dai, Yiming Sun, Yiheng Chen +4Forecasting Benchmarks Scientific ML
University of Alabama Tuscaloosa, USA · University of Pittsburgh Pittsburgh, USA
SocietyBench: Forecasting Counterfactual Social-World Evolution
Aug 4, 2026Zhenran Wang, Zhonghan Bian, Jinsong Li +1Counterfactual Evaluation Forecasting Benchmarks
WorldCup Arena: Prospective, Leakage-Free Evaluation of Frontier LLMs on a Live Tournament
Aug 4, 2026Zhenran Wang, Zhonghan Bian, Jinsong Li +1LLM Evaluation Forecasting Benchmarks
AI World Cup 2026: Benchmarking Large Language Models for End-to-End Football Tournament Prediction
Aug 4, 2026Jonaid Shianifar, Iias FaiudLLM Evaluation Forecasting Benchmarks
AI World Cup Project
FinVerse: Financial Time-Series Benchmark
Aug 4, 2026Jaehoon Lee, Jun Seo, Seunghan Lee +9Benchmark Design Financial Forecasting
LG AI Research
ReasonCast: Towards Explainable Time Series Forecasting with Reasoning
Aug 3, 2026Seunghan Lee, Jun Seo, Jaehoon Lee +9Explainable Time Series Forecasting Time Series Reasoning
LG AI Research
Benchmarking ConvLSTM for One-Day-Ahead IMDAA Rainfall-Field Prediction across Four Indian Cities
Jul 29, 2026Tanmay Ghosh, Shaurabh Anand, Rakesh Gomaji Nannewar +1Convolutional RNNs Precipitation Forecasting
National Institute of Advanced Studies, Indian Institute of Science Campus, Bengaluru, India. · School of Development, Azim Premji University, Bengaluru, India. · Complex Systems Programme, National Institute of Advanced Studies, Indian Institute of Science Campus, Bengaluru, India.
LLM-SoccerArena: Benchmarking LLMs on Real-World Predictions in Sports
Jul 27, 2026Jonas Schröder, Jonas Schweisthal, Oliver Müller +2LLM Evaluation Forecasting Benchmarks
MCML & LMU Munich Munich, Germany · Paderborn University Paderborn, Germany · University of Cologne Cologne, Germany
Variational Quantum Conditional Boltzmann Machines for Time-Series Forecasting: Architectures, Symmetric Hyperparameter Evaluation, and a Nonlinear Benchmark
Jul 27, 2026Gerhard Hellstern, Danyal Maheshwari, Martin Zaefferer +2Time Series Forecasting Forecasting Benchmarks
Centre of Finance DHBW Stuttgart Stuttgart, Germany · Zentrum für Digitale Innovationen DHBW Ravensburg Ravensburg, Germany · DATEV eG Nürnberg, Germany
GlucoTune: A Unified Framework for Blood Glucose Preprocessing, Forecasting, and Benchmarking in Diabetes
Jul 23, 2026Davide Marelli, Giorgia Rigamonti, Mirko Paolo Barbato +1Type 1 Diabetes Time Series Forecasting
Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milano 20126, Italy
Time Series Network Utilization KPI Forecasting Using Advanced AI/ML Models
Jul 22, 2026Niraj Gadhe, Kirti Bhardwaj, Moulik Jain +2Time Series Forecasting Forecasting Benchmarks
Independent Researcher
WorldCupArena: Fine-Grained Evaluation of Language Models and Deep-Research Agents on Football Forecasting
Jul 20, 2026Zhaokai Wang, Tianlin Gui, Jiayuan Rao +3LLM Evaluation Forecasting Benchmarks
Shanghai Jiao Tong University · Shanghai, China · Nanjing University +4
FIFA World Cup 2026 as a Contamination-Free Benchmark for LLM Forecasting Agents: Four Models, a Bookmaker, and 104 Matches
Jul 20, 2026Jiacheng Ding, Cong Guo, Jason XuLLM Agent Evaluation Forecasting Benchmarks
University of Memphis Memphis, Tennessee, USA · QuantaInsight Seattle, Washington, USA
A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods
Jul 17, 2026Matthias Hertel, Sebastian Pütz, Jonathan Kolar +3Time Series Forecasting Forecasting Benchmarks
Karlsruhe Institute of Technology, Germany · Helmholtz AI, Germany
Hindcast: Replaying Prediction Markets to Evaluate LLM Forecasters
Jul 15, 2026Xiao Ye, Jacob Dineen, Evan Zhu +3LLM Evaluation Forecasting Benchmarks
School of Computing and Augmented Intelligence, Arizona State University
When Directional Accuracy Lies: A Base-Rate-Honest Benchmark for LoRA-Adapted TimesFM on Equity Forecasting
Jul 14, 2026Taizhen CheungFinancial Forecasting Time Series Forecasting
Institutional Equity Holdings Prediction Using Node Affinities of Dynamic Graphs
Jul 13, 2026Emad Izadifar, Zahed RahmatiTemporal GNNs Link Prediction
aDepartment of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran
When Classical Baselines Are Tuned as Carefully as the Quantum Model, Does Quantum Reservoir Computing Still Win?
Jul 10, 2026Tushar PandeyTime Series Forecasting Forecasting Benchmarks
Texas A&M University
Evaluating the Generalizability of Foundation Models for Extreme Environmental Events: Case Study of California Wildfire PM2.5
Jul 8, 2026Yongcan Huang, Li Jiang, Ze Yu LiuWildfire Forecasting OOD Generalization
College of Engineering, University of Georgia, Athens, Georgia, USA · Information System Department, UMBC, Baltimore, Maryland, USA · College of Graduate and Professional Studies, Trine University, Angola, Indiana, USA +1
Rethinking Multimodal Time-Series Forecasting Evaluation
Jul 8, 2026Haoxin Liu, Yichen Zhou, Rajat Sen +2Multivariate Time Series Forecasting Zero-Shot Time Series Forecasting
Georgia Institute of Technology · Google Research
When Do Foundation Models Pay Off? A Break-Even Analysis of Pretrained Time Series Forecasters
Jul 6, 2026Nicholas Tan Jerome, Frank SimonZero-Shot Time Series Forecasting Time Series Forecasting
Karlsruhe Institute of Technology (KIT) Eggenstein-Leopoldshafen, Germany
The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error
Jul 5, 2026Hugo MoreiraTime Series Forecasting Forecasting Benchmarks
1CIES - Centro de Investigação e Estudos de Sociologia, Iscte - Instituto Universitário de Lisboa, Lisbon, Portugal.
Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence
Jul 2, 2026Zhenghua Pan, Ahmed Aziz EzzatZero-Shot Time Series Forecasting Electricity Price Forecasting
Department of Industrial & Systems Engineering, Rutgers, The State University of New Jersey, USA.
Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins
Jul 1, 2026Md Rakibul Haque, Shireen Elhabian, Warren Woodrich PettineTime Series Forecasting Forecasting Benchmarks
Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, 84112, UT, USA. · Kahlert School of Computing, University of Utah, Salt Lake City, 84112, UT, USA. · Department of Psychiatry, University of Utah, Salt Lake City, UT, USA.
Diversity is the Strength of the AI Crowd
Jun 29, 2026Matthew Aitchison, Scott Jeen, Toby Shevlane +1Forecasting Benchmarks Language Model Ensembles
1Mantic Technologies, London, UK.

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Benchmarking Time Series Generation Methods for Privacy-Preserving Forecasting

Luis Amorim, Vitor Cerqueira, Moises Santos, Paulo J. Azevedo+1

Faculdade de Engenharia da Universidade do Porto, Porto, Portugal · University of Coimbra, Coimbra, Portugal · Laboratory for Artificial Intelligence and Computer Science (LIACC), Portugal+3

Synthetic Data Generation Time Series Forecasting Time Series Generation Forecasting Benchmarks Privacy-Preserving ML Synthetic Data Evaluation

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Time series forecasting in privacy-sensitive domains often requires training models on released data rather than original observations. Synthetic time series generation has been developed primarily for data augmentation, where generated series supplement the original training set. How well these methods perform when fully replacing the original data - and how much privacy risk the released series carry - remains underexplored. We address this gap through a benchmark evaluating synthetic generation methods and noise-based anonymization baselines under a Train on Synthetic, Test on Real (TSTR) protocol. We jointly assess forecasting performance and distance-based empirical privacy risk across seven datasets, characterizing the trade-off between these objectives. We also introduce Grasynda-P, a privacy-motivated extension of the graph-based generator Grasynda, incorporating matrix ensembling and kernel density estimation. Our results show that: (1) no generation method fully substitutes for original training data; (2) noise-based anonymization yields the strongest privacy but the worst forecasting performance; (3) simple transformation-based generators outperform deep generative models for forecasting in this setting; and (4) Grasynda-P lies on the Pareto frontier, achieving competitive forecasting with stronger privacy separation than other generators. This benchmark establishes a reference point for evaluating and developing new privacy-aware synthetic time series generation methods.

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Latest papers 147