Enterprise AI Analysis
CONVERSATIONAL TIME SERIES FOUNDATION MODELS: TOWARDS EXPLAINABLE AND EFFECTIVE FORECASTING
This paper introduces TSOrchestra, a novel framework that positions Large Language Models (LLMs) as intelligent judges to orchestrate ensembles of specialized time series forecasting models. Unlike direct LLM applications which struggle with numerical precision, TSOrchestra leverages LLMs' reasoning capabilities for evaluating, explaining, and strategically coordinating forecasts.
Transforming Time Series Forecasting with Explainable AI
TSOrchestra redefines enterprise time series forecasting by integrating advanced LLM reasoning with numerical precision. This leads to more accurate, robust, and interpretable predictions, crucial for strategic decision-making in finance, supply chain, energy, and beyond. Its ability to provide causally-grounded explanations fosters trust and enables practitioners to validate and act upon forecasts with greater confidence. The adaptive, multi-turn conversational approach ensures forecasts remain relevant even in dynamic market conditions, significantly reducing risks associated with traditional black-box models.
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CRPS Improvement Over Baseline
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MASE Rank on GIFT-Eval
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Datasets Evaluated (97 settings)
Key Enterprise Benefits:
- ✓ Enhanced Forecasting Accuracy: Outperforms state-of-the-art foundation models.
- ✓ Causally-Grounded Explainability: Provides transparent reasoning for predictions.
- ✓ Adaptive Optimization: Dynamically refuses forecasting strategy in real-time.
- ✓ Robust Decision-Making: Mitigates risks in non-stationary environments.
- ✓ Resource-Efficient Deployment: Utilizes fine-tuned compact LLMs for scalability.
Deep Analysis & Enterprise Applications
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TSOrchestra: Iterative Reasoning & Optimization Flow
The TSOrchestra framework uses an LLM agent to guide the ensemble optimization process through several iterative steps, enhancing both accuracy and interpretability.
Initial Ensemble Weights (SLSQP)
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LLM Analysis & Decision
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Performance-Based Adjustment
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LLM Analysis & Decision
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Refined Optimization
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LLM Confidence & Key Insight
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Final Outcome & Interpretability Reflection
CRPS & MASE Performance Boost
25.5% Average CRPS & MASE ImprovementTSOrchestra achieves significant performance improvements over leading time series foundation models on both CRPS (Continuous Ranked Probability Score) and MASE (Mean Absolute Scaled Error) metrics.
LLM-Guided vs. Traditional Optimization
TSOrchestra's LLM-guided approach provides superior benefits compared to traditional, non-agentic optimization methods, particularly in dynamic and complex forecasting scenarios.
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Qualitative Analysis: Failure Detection in Non-Stationary Regimes (Mirage Trend)
TSOrchestra's agentic reasoning acts as a robustness filter, identifying and correcting brittle numerical solutions in noisy, non-stationary environments.
Scenario: Consider a financial time series transitioning from a stable trending regime to a highly volatile, mean-reverting regime. A sharp, temporary price spike occurs due to volatility, not a sustained trend.
Baseline Failure: Standard numerical optimization (SLSQP) interprets the volatility spike as a strong upward trend, assigning high weights to trend-focused models (e.g., Moirai). This leads to overfitting and failure to generalize to the subsequent volatile regime.
Agent Correction: TSOrchestra's faithfulness mechanism triggers a causal decomposition. It detects that while the trend-focused model performs well numerically, its SHAP-derived causal contribution to the trend component is near zero, and residual variance is high. The agent identifies this as a 'Mirage Trend'—spurious correlation—and rejects the trend-heavy weights, instead prioritizing metrics robust to outliers (e.g., sMAPE) or models specialized in local adaptability (e.g., Toto).
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Annual Savings
Hours Reclaimed Annually
Your TSOrchestra Implementation Roadmap
A structured approach to integrate explainable time series forecasting into your operations.
Data Preparation & Model Selection
Gather and preprocess time series data, select candidate foundation models. Establish initial cross-validation strategy.
Duration: 1-2 weeks
LLM Fine-tuning (SFT & GRPO)
Train the LLM agent using supervised fine-tuning (SFT) for structured decision protocols, followed by Group Relative Policy Optimization (GRPO) for performance optimization and faithfulness alignment.
Duration: 2-4 weeks
Iterative Ensemble Optimization
Deploy the LLM-guided system to iteratively refine ensemble weights, perform forward-looking assessments, and generate causally-grounded explanations.
Duration: 2-3 weeks
Validation & Deployment
Validate the system on production data, ensure robustness in non-stationary environments, and integrate into existing forecasting pipelines.
Duration: 1 week
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