TimeCopilot

• URL: http://arxiv.org/abs/2509.00616v3
• Date: Fri, 07 Nov 2025 01:43:57 GMT
• Title: TimeCopilot
• Authors: Azul Garza, Renée Rosillo,
• Abstract summary: We introduce TimeCopilot, the first open-source agentic framework for forecasting.<n>It combines multiple Time Series Foundation Models (TSFMs) with Large Language Models (LLMs) through a single unified API.<n>TimeCopilot automates the forecasting pipeline: feature analysis, model selection, cross-validation, and forecast generation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce TimeCopilot, the first open-source agentic framework for forecasting that combines multiple Time Series Foundation Models (TSFMs) with Large Language Models (LLMs) through a single unified API. TimeCopilot automates the forecasting pipeline: feature analysis, model selection, cross-validation, and forecast generation, while providing natural language explanations and supporting direct queries about the future. The framework is LLM-agnostic, compatible with both commercial and open-source models, and supports ensembles across diverse forecasting families. Results on the large-scale GIFT-Eval benchmark show that TimeCopilot achieves state-of-the-art probabilistic forecasting performance at low cost. Our framework provides a practical foundation for reproducible, explainable, and accessible agentic forecasting systems.

Related papers

• ARIES: Relation Assessment and Model Recommendation for Deep Time Series Forecasting [54.57031153712623]
  ARIES is a framework for assessing relation between time series properties and modeling strategies.<n>We propose the first deep forecasting model recommender, capable of providing interpretable suggestions for real-world time series.
  arXiv  Detail & Related papers  (2025-09-07T13:57:14Z)
• Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting [64.45587649141842]
  Time-series forecasting plays a critical role in many real-world applications.<n>No single model consistently outperforms others across different test samples, but instead (ii) each model excels in specific cases.<n>We introduce TimeFuse, a framework for collective time-series forecasting with sample-level adaptive fusion of heterogeneous models.
  arXiv  Detail & Related papers  (2025-05-24T00:45:07Z)
• Benchmarking Time Series Forecasting Models: From Statistical Techniques to Foundation Models in Real-World Applications [0.0]
  Time series forecasting is essential for operational intelligence in the hospitality industry.<n>This study evaluates the performance of statistical, machine learning (ML), deep learning, and foundation models in forecasting hourly sales over a 14-day horizon.
  arXiv  Detail & Related papers  (2025-02-05T17:30:31Z)
• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context.<n>We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters.<n>We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks [17.64833210797824]
  We propose a probabilistic weather forecasting model called Graph-EFM. The model combines a flexible latent-variable formulation with the successful graph-based forecasting framework. Ensemble forecasts from Graph-EFM achieve equivalent or lower errors than comparable deterministic models.
  arXiv  Detail & Related papers  (2024-06-07T09:01:25Z)
• GenCast: Diffusion-based ensemble forecasting for medium-range weather [10.845679586464026]
  We introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world. GenCast generates an ensemble of 15-day global forecasts, at 12-hour steps and 0.25 degree latitude-longitude, for over 80 surface and atmospheric variables in 8 minutes. It has greater skill than ENS on 97.4% of 1320 targets we evaluated, and better predicts extreme weather, tropical cyclones, and wind power production.
  arXiv  Detail & Related papers  (2023-12-25T19:30:06Z)
• Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting [54.04430089029033]
  We present Lag-Llama, a general-purpose foundation model for time series forecasting based on a decoder-only transformer architecture. Lag-Llama is pretrained on a large corpus of diverse time series data from several domains, and demonstrates strong zero-shot generalization capabilities. When fine-tuned on relatively small fractions of such previously unseen datasets, Lag-Llama achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2023-10-12T12:29:32Z)
• AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting [80.14147131520556]
  AutoGluon-TimeSeries is an open-source AutoML library for probabilistic time series forecasting. It generates accurate point and quantile forecasts with just 3 lines of Python code.
  arXiv  Detail & Related papers  (2023-08-10T13:28:59Z)
• Masked Multi-Step Probabilistic Forecasting for Short-to-Mid-Term Electricity Demand [7.544120398993689]
  Masked Multi-Step Multi Probabilistic Forecasting (MMMPF) is a novel and general framework to train any neural network model. It combines both the temporal information from the past and the known information about the future to make probabilistic predictions. MMMPF can also generate desired quantiles to capture uncertainty and enable probabilistic planning for grid of the future.
  arXiv  Detail & Related papers  (2023-02-14T04:09:03Z)
• Masked Multi-Step Multivariate Time Series Forecasting with Future Information [7.544120398993689]
  In many real-world forecasting scenarios, some future information is known, e.g., the weather information when making a short-to-mid-term electricity demand forecast. To overcome the limitations of existing approaches, we propose MMMF, a framework to train any neural network model capable of generating a sequence of outputs. Experiments are performed on two real-world datasets for (1) mid-term electricity demand forecasting, and (2) two-month ahead flight departures.
  arXiv  Detail & Related papers  (2022-09-28T20:49:11Z)
• Learning Interpretable Deep State Space Model for Probabilistic Time Series Forecasting [98.57851612518758]
  Probabilistic time series forecasting involves estimating the distribution of future based on its history. We propose a deep state space model for probabilistic time series forecasting whereby the non-linear emission model and transition model are parameterized by networks. We show in experiments that our model produces accurate and sharp probabilistic forecasts.
  arXiv  Detail & Related papers  (2021-01-31T06:49:33Z)
• A framework for probabilistic weather forecast post-processing across models and lead times using machine learning [3.1542695050861544]
  We show how to bridge the gap between sets of separate forecasts from NWP models and the 'ideal' forecast for decision support. We use Quantile Regression Forests to learn the error profile of each numerical model, and use these to apply empirically-derived probability distributions to forecasts. Second, we combine these probabilistic forecasts using quantile averaging. Third, we interpolate between the aggregate quantiles in order to generate a full predictive distribution.
  arXiv  Detail & Related papers  (2020-05-06T16:46:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.