arXiv AI
Jul 3
PreScience: A Dataset and Benchmark for Scientific Forecasting
arXiv:2602. 20459v2 Announce Type: replace Abstract: Can AI systems trained on the existing scientific record forecast the advances that will follow?
Benchmark Datasets for Lead-Lag Forecasting on Social Platforms
arXiv:2511. 03877v2 Announce Type: replace Abstract: Social and collaborative platforms emit multivariate time-series traces in which early interactions -- such as views, likes, or downloads -- are followed, sometimes months or years later, by higher impact like citations, sales, or reviews.
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arXiv AI
Jun 29
Benchmarking Multi-Modal Graph-based Social Media Popularity Prediction
arXiv:2606. 27539v1 Announce Type: cross Abstract: Social media popularity prediction aims to forecast the future reach or influence of online content from early-stage observations.
arXiv AI
Aug 7
Multivariate Time Series Forecasting needs Cross Variable Loss
arXiv:2608. 05742v1 Announce Type: cross Abstract: Multivariate time series forecasting presents unique challenges because future variables often co-evolve under shared system dynamics.
arXiv Machine Learning
Jul 13
Temporal Knowledge Graph Forecasting under Distribution Shifts: A Synthetic Evaluation
arXiv:2607. 09232v1 Announce Type: new Abstract: Temporal knowledge graphs (TKGs) represent evolving relational systems, whose underlying data-generating processes often change over time.
Hugging Face Trending Papers
Jul 7
RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
Recent years have witnessed the emergence of multivariate modeling using time series foundation models (TSFMs), which achieve advanced zero-shot generalization. Modern multivariate TSFMs are predominantly pretrained on multivariate synthetic data, which is easier to scale but may fail to capture the complex temporal dynamics and cross-variable relationships present in real-world time series.
arXiv Machine Learning
Jun 15
Detecting Lookahead Bias in LLM Forecasts
arXiv:2512. 23847v2 Announce Type: replace-cross Abstract: We develop a statistical procedure to detect lookahead bias in economic forecasts generated by large language models (LLMs).
arXiv Machine Learning
Jun 26
How Good Can Linear Models Be for Time-Series Forecasting?
arXiv:2606. 27282v1 Announce Type: new Abstract: Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy.
arXiv AI
Jul 8
RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
arXiv:2607. 06504v1 Announce Type: new Abstract: Recent years have witnessed the emergence of multivariate modeling using time series foundation models (TSFMs), which achieve advanced zero-shot generalization.
arXiv AI
Jul 15
Scaling Point-in-Time Language Models
arXiv:2607. 11889v1 Announce Type: cross Abstract: Large language models trained on unrestricted internet corpora inevitably embed information from the future, introducing lookahead bias that compromises the validity of backtests and causal inference in finance and the social sciences.
arXiv Machine Learning
Jun 11
NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Series
arXiv:2510. 22397v2 Announce Type: replace-cross Abstract: Network operators monitor their infrastructure by collecting telemetry data such as packet counts, byte rates, or flow volumes, yet answering the questions that effective operations demand -- forecasting future load, diagnosing and characterizing anomalies, and searching for and retrieving historical precedents -- requires more than raw measurements.
Hugging Face Trending Papers
Aug 4
SocietyBench: Forecasting Counterfactual Social-World Evolution
Large language models (LLMs), and the agents built on top of them, are now benchmarked heavily on whether they can finish a task -- fix a bug, drive a browser, operate a GUI. A complementary social ability, namely how well a model understands and forecasts the way real social events unfold, has barely been measured.
arXiv AI
Jun 26
Thinking Like a Scientist? A Structural Study of LLM-Generated Research Methods
arXiv:2606. 26130v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to guide research methodology, yet their default methodological tendencies under minimal prompting remain unclear.