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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LiveHouse-TS: An Open-world Living Benchmark for Time Series Foundation Models
Time Series Foundation Models (TSFMs) have recently emerged as a highly promising paradigm for cross-domain zero-shot forecasting. However, existing evaluation protocols predominantly rely on static benchmarks with fixed historical test windows.
arXiv AI
Aug 19
LiveHouse-TS: An Open-world Living Benchmark for Time Series Foundation Models
LiveHouse-TS introduces an open‑world living benchmark for Time Series Foundation Models, evaluating them prequentially on real future data rather than static test windows. The benchmark captures continuous performance across seasonal changes, distribution shifts, and unexpected events, providing a more realistic assessment of model robustness. Experiments across 11 domains and 17 datasets show that model rankings can dramatically change under this live protocol.
arXiv Machine Learning
Sep 23
GitScholar: A Dataset for Predicting AI Research Impact from GitHub Engagement
arXiv:2609.26361v1 Announce Type: cross Abstract: With the rapid pace of AI research and the hundreds of daily new publications, staying up-to-date with the latest developments has become increasingl...
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
2d ago
The Nixtlaverse: An Open-Source Ecosystem for Forecasting
arXiv:2609.39741v1 Announce Type: new Abstract: Large forecasting applications often combine statistical, machine-learning, and neural models. These families solve the same problem but differ in fitt...
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.