ENTERPRISE AI ANALYSIS
Time Series Foundation Models for Process Model Forecasting
This deep dive explores the application of Time Series Foundation Models (TSFMs) to Process Model Forecasting (PMF), a critical advancement for understanding and predicting the evolution of business processes.
Executive Impact: Transforming Process Intelligence
TSFMs offer a significant leap in process model forecasting, providing more accurate and robust predictions than traditional methods. This translates into tangible benefits across the enterprise.
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Reduction in Forecasting Error
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Diverse Event Logs Analyzed
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TSFM Models Evaluated
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Data Efficiency Improvement
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
TSFM Performance Benchmarking
Process Model Evolution
Adaptation Strategies & Overfitting
TSFMs, even in a zero-shot setting, substantially outperform traditional baselines across various process dynamics. This highlights their robust generalization capability for process model forecasting.
Newer and larger TSFMs generally provide better predictions, attributed to architectural improvements and more diverse pre-training data. While fine-tuning can offer additional gains, these are often marginal and dataset-dependent, with zero-shot remaining a strong default.
28%
Max MAE Reduction Zero-Shot (Sepsis)
| Feature | Traditional Models | TSFMs (Foundation Models) |
|---|---|---|
| Generalization Capability | Limited to specific data patterns, often requiring re-training. |
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| Data Efficiency | Requires significant task-specific data for optimal performance. |
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| Temporal Pattern Recognition | Struggle with sparse, heterogeneous, and complex temporal patterns. |
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| Adaptation Methods | Full re-training or complex feature engineering. |
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Process Model Forecasting aims to predict how the control-flow structure of a process evolves over time, using directly-follows (DF) relation frequencies as multivariate time series. These time series are often sparse, heterogeneous, and exhibit complex seasonal and cyclical effects.
TSFMs excel at capturing these complex, dynamic patterns in DF time series, providing insights into future process behaviors that instance-level monitoring cannot.
Enterprise Process Flow
Event Logs
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DFG Derivation (Time Window)
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Multivariate DF Time Series
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TSFM Forecasting
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Forecasted Process Model
7
Days Forecast Horizon
Our study systematically compares zero-shot, LoRA-based PEFT, and full fine-tuning. Zero-shot TSFMs consistently outperform baselines, demonstrating strong transfer learning.
While fine-tuning can improve accuracy, the gains are often marginal and dataset-dependent. On smaller or more complex datasets, fine-tuning risks overfitting and may even degrade performance, making zero-shot a robust default.
Zero-Shot
Recommended Default Strategy
Case Study: Hospital Billing Log Performance
The Hospital Billing log, characterized by clear trends, large shifting ratios, and a long time span, presented a significant challenge. Traditional models struggled to capture its complex long-term structural changes. TSFMs, particularly in zero-shot mode, showed notable improvement in Entropic Relevance (ER) compared to models trained on the log's own training set. This success demonstrates TSFMs' ability to recognize and forecast appropriate dynamics even with diverging characteristics.
ER Improvement (Hospital Billing): 50%+
Advanced ROI Calculator
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Estimated Annual Savings
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Annual Hours Reclaimed
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Implementation Roadmap
Our proven framework ensures a smooth transition to AI-powered process intelligence, tailored to your organization's unique needs.
Phase 1: Discovery & Strategy
In-depth analysis of your existing process monitoring landscape, data availability, and business objectives to define a clear AI strategy for PMF.
Phase 2: Data Integration & Model Setup
Secure integration of your event logs, preprocessing of DF time series, and configuration of selected TSFMs (zero-shot or fine-tuned).
Phase 3: Validation & Customization
Rigorous testing and validation of the forecasting models against historical data, followed by fine-tuning and customization for optimal performance.
Phase 4: Deployment & Training
Seamless deployment of the PMF solution within your infrastructure and comprehensive training for your team on leveraging process model forecasts.
Phase 5: Continuous Optimization
Ongoing monitoring, performance evaluation, and iterative improvements to ensure your AI-driven process forecasting remains state-of-the-art.
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