Skip to main content
Search

# Chronos

3 related articles

Subscribe to updates
  1. arXiv

    Power-system dynamic simulation gets a fast stand-in: fine-tuned Chronos-2 beats a traditional AI model on error and runs 563 times faster

    The pretrained time-series foundation model Chronos-2 was tested as a surrogate for power-system dynamic simulation. On 1,800 load-disturbance samples from an IEEE 9-bus system, retrieval-augmented in-context learning beat random reference selection, and fine-tuned Chronos-2 had lower errors than a traditional spatiotemporal graph neural network baseline on the 178 held-out samples, with power-balance regularization improving every reported metric except voltage-angle MAE. Its 35.5 ms inference was 563 times faster than the 20 s ANDES simulation.

    Sandy Miguel（First author）
    Time-Series Foundation ModelChronosPower System Dynamic SimulationIn-Context LearningFine-tuning
  2. arXiv

    Code agents learn to read a repository's pull-request history, lifting SWE-Bench Verified resolution from 69.2% to 72.9%

    Code agents solving repository-level tasks usually read only the current source tree, leaving the design decisions and compatibility constraints recorded in merged pull requests unused. Chronos distills each merged pull request into a structured experience card and links the cards in a typed graph of code, developer-intent and organizational relations, then retrieves by semantic entry plus weighted multi-hop expansion. On SWE-Bench Verified the full workflow improves SWE-Agent on all six evaluated backbones, raising mean resolution from 69.2% to 72.9% and reaching 79.8% in the strongest configuration.

    Xin Yin（First author）
    ChronosGraph-Grounded RetrievalCoding AgentsProgram Repair
  3. bioRxiv

    Time-series foundation modeling enables accurate lake ecosystem forecasting

    Using up to 30 years of monthly monitoring records from two ecologically contrasting Japanese lakes, the deep-stratified Lake Biwa and the shallow nutrient-rich Lake Kasumigaura, this study benchmarked two time-series foundation models, the Transformer-based Chronos-T5 and the probabilistic Lag-Llama, against 15 statistical (AR, ARIMA, SARIMA, Prophet), machine learning (Random Forest, XGBoost, KNN, SVR), and deep learning (LSTM, CNN, TCN, and SSA-hybrid) approaches, finding that fine-tuned Chronos-T5 ranked first across all six monitoring sites with R2 > 0.80-1.00 for dissolved oxygen, water temperature, pH, and nutrients, while chlorophyll-a remained below R2 0.

    Kyoto University
    Asuka Matsushita（First author）Hideyuki Doi（Corresponding author）
    ChronosTime-Series Foundation ModelEcological Forecasting

You’re all caught up