TimeCopilot: Unified Agentic Forecasting Framework

• TimeCopilot is an agentic forecasting framework that unifies TSFMs and LLMs under a single API to deliver robust, probabilistic time series predictions.
• The system automates feature extraction, model selection, and ensemble forecasting while ensuring reproducibility through state-of-the-art performance metrics like CRPS and MASE.
• Beyond forecasting, earlier versions of TimeCopilot enable decentralized synchronization, teleoperation, and MPC-based quadrotor control, demonstrating versatility in real-time applications.

TimeCopilot is a multifaceted term referring to distinct, technically advanced systems across forecasting, robotics, time synchronization, and time-tagged measurement analysis. In its most recent and direct usage (Garza et al., 30 Aug 2025), TimeCopilot denotes an open-source agentic framework for probabilistic time series forecasting that orchestrates Time Series Foundation Models (TSFMs) and LLMs via a unified API. Historic and conceptual deployments of the name relate to time synchronization schemes in distributed systems, bilateral teleoperation for robot learning, quadrotor flight control, and high-throughput time-tagged data analysis. The following sections clarify the principal instantiations, technological underpinnings, and application domains of TimeCopilot.

1. Agentic Forecasting Framework Integrating TSFMs and LLMs

The contemporary primary definition of TimeCopilot specifies an open-source agentic forecasting system that unifies multiple TSFMs—comprising neural models (AutoNHITS, Toto, Moirai, Sundial) and traditional statistical baselines (AutoARIMA, SeasonalNaive, Prophet)—under a single API orchestrated through LLM-based workflow management (Garza et al., 30 Aug 2025). The framework utilizes the reasoning and planning faculties of an LLM agent to automate time series analysis, model selection, cross-validation, and generation of both numerical forecasts and natural language explanations.

The forecasting pipeline operates as follows:

Step	Technical Details	Role in Pipeline
Feature Analysis	Computes diagnostics (trend, seasonality, etc.)	Data characterization
Candidate Model Selection	Iterative proposal via LLM agent	Explores model families
Cross-validation/Benchmarking	Evaluates out-of-sample performance	Ensures reproducibility
Ensemble Forecasting and Quantile Calibration	MedianEnsemble, isotonic regression	Robust probabilistic output
Explanation Generation	Natural language via LLM	Model introspection

The LLM bridges high-level queries ("What will air passenger counts be in 12 months?") to forecasting modules, providing rationale on model choice, uncertainty, and diagnostics. All TSFMs and algorithms are exposed through a unified API, facilitating interoperability. MedianEnsemble with isotonic regression enforces monotonic quantile consistency in probabilistic outputs.

2. Automated Pipeline, Reproducibility, and Performance Metrics

TimeCopilot automates canonical forecasting workflow components:

• Feature Extraction: Uses functions similar to tsfeatures for noise, trend, seasonal decomposition.
• Model Evaluation: Cross-validation across multiple unseen slices to prevent test leakage.
• Adaptive Complexity: Defaults to lightweight statistical models unless diagnostics justify TSFM escalation.

Performance is benchmarked on the GIFT-Eval corpus: 24 datasets, over 144,000 time series, 177 million points. Quantitative results show TimeCopilot reaches state-of-the-art probabilistic performance—CRPS metric—using efficient GPU-based inference at low cost (distributed computation: ~$24). MASE is the competitive point forecast metric. Systematic outperformance is exhibited over baselines and competitive ensemble alternatives.

3. LLM-Agnostic Design, Explainability, and API Flexibility

TimeCopilot's agentic control layer is LLM-agnostic:

• Interoperability: Compatible with proprietary APIs (GPT-4) and open-source models (LLaMA, DeepSeek).
• API Uniformity: All TSFMs and ensemble methods are wrapped to share a uniform interface, enabling modular extension.
• Explainability: The LLM not only executes orchestration but serves as the explanation engine. Generated forecasts accompany natural language justifications on model selection, uncertainty quantification, and feature influence.

Users may pose follow-up queries about outcomes; the system supports conversational interaction, enhancing transparency and model auditability:

Forecast = f(LLM(query, diagnostics), TSFM(data, hyperparameters))

A plausible implication is increased trust and interpretability in forecasting workflows applied to high-stakes domains.

4. Technical Innovations Across Related Domains

In prior conceptualizations, TimeCopilot has denoted:

• Decentralized Time Synchronization: Following the ChronoSync protocol (Zegers et al., 6 Apr 2025), TimeCopilot embodies distributed drift-corrected clock synchronization using consensus-based controllers over static, undirected multi-agent graphs. Each agent steers software clocks via control laws incorporating neighbor samples, drift estimation, and Lyapunov-stable hybrid updates. Asynchronous timer mechanisms trigger broadcasts, yielding robustness to environmental disturbance and communication noise.
• Bilateral Teleoperation for Robot Learning: As HACTS (Xu et al., 31 Mar 2025), TimeCopilot signifies human-as-copilot teleoperation hardware supporting bilateral synchronization (leader-follower and follower-leader modes). The system allows real-time intervention and collection of action-correction data, improving imitation learning and reinforcement learning efficacy. Kinematic mapping is achieved via Denavit–Hartenberg parameters, with control laws managing corrective feedback. Performance gains are validated in manipulation tasks (OpenBox, SteamBun, UprightMug) and out-of-distribution scenarios.
• Model Predictive Contouring Control (MPCC) in Quadrotor Flight: TimeCopilot (Romero et al., 2021) as an MPCC-based controller jointly optimizes contour/lag error and time progression along arc-length parameterized reference paths. The system outperforms state-of-the-art MPC and human pilots in drone racing, achieving high speed (up to 60 km/h), robust gate tracking under delay, and real-time replanning.

5. Comparative Perspective: Flexibility, Decentralization, Real-Time Operation

TimeCopilot’s implementations are unified by three recurring themes:

• Agentic Reasoning: The system orchestrates complex model, control, or synchronization tasks via explicit algorithmic or learned agents (LLM agent, consensus controller).
• Automation and Real-Time Capability: Forecasting and control pipelines execute with minimal human intervention, supporting both reproducibility and instantaneous feedback (real-time replanning in flight, online explanation generation, bilateral teleoperation).
• Decentralization and Robustness: Particularly in time synchronization settings, the system resists single-point-of-failure and accommodates asynchronous communication. In the forecasting framework, modular API interfaces allow for seamless expansion and ensemble construction.

Applications extend to air passenger forecasting, energy and finance, supply chain management, autonomy in drone flight, distributed sensor networks, and human-robot interaction.

6. Practical Deployment and Research Implications

TimeCopilot’s modularity and open-source philosophy facilitate adoption in research and industry. For forecasting tasks, the system provides a reproducible foundation supporting ensemble methods, explainable AI, and standardized evaluation. In robotics and synchronization, it enables efficient data collection, intervention, and distributed consensus without heavy infrastructure or vendor lock-in.

Technical rigor is emphasized in each domain: API uniformity for forecasting reproducibility, bilateral feedback loops for manipulation reliability, Lyapunov-stable controllers for distributed timekeeping, and optimized sorting/correlation algorithms for time-tagged event analysis.

In summary, TimeCopilot encompasses a suite of agent-driven methodologies spanning probabilistic forecasting, distributed synchronization, teleoperation, and autonomous control, each characterized by technical innovation, reproducibility, and scalability in complex real-world domains.