Forecasting Model Card

• Forecasting model cards are structured artifacts that document all critical aspects of a time-series forecasting model, including data, methodology, and performance metrics.
• They promote transparency and reproducibility by clearly outlining model architecture, validation techniques, and deployment constraints.
• They facilitate robust evaluation and risk management in high-stakes applications like financial planning, supply chain, and public health forecasting.

A forecasting model card is a structured, detailed artifact that documents the critical aspects of a time-series forecasting model. Its primary purpose is to ensure transparency, reproducibility, performance traceability, explainability, and responsible deployment within technical and business contexts. The model card presents all information required to understand, audit, deploy, and maintain forecasting models, supporting rigorous evaluation, risk management, and ongoing improvement.

1. Motivation and Scope

Forecasting model cards originated to fill the need for transparent disclosures about model structure, data provenance, validation methodologies, deployment context, limitations, and ethical concerns. Their adoption aligns with the broader movement toward model documentation in regulated, high-stakes, or operationally critical applications such as supply chain management, financial planning, epidemiological surveillance, and enterprise resource forecasting.

A model card encapsulates:

• A description of target forecasting tasks (e.g., 30-day retail demand, hierarchical revenue projections)
• Model architecture and innovations
• Intended usage scenarios and out-of-scope cases
• Data sources, preprocessing, and training/validation methodologies
• Performance metrics and quantitative results
• Explanation, interpretability methods, and findings
• Ethical considerations, limitations, and deployment guidelines

Exemplars include MCDFN (Jahin et al., 2024), a hybrid deep learning model for retail demand forecasting, and the Bayesian hierarchical reconciliation model for structured enterprise time series (Novak et al., 2017).

2. Structural Template and Key Components

Forecasting model cards typically present a standardized structure comprising the following sections:

Section	Typical Content	Example Reference
Model Overview	Name, version, architectural summary, key innovations	MCDFN (Jahin et al., 2024)
Intended Use	Primary tasks, deployment context, exclusion criteria	MCDFN (Jahin et al., 2024)
Data and Feature Engineering	Source, time span, frequency, preprocessing, feature construction	DemandLens (Pillai et al., 14 Sep 2025)
Metrics and Quantitative Results	Definitions (MSE, MAE, MAPE, etc.), model performance, baselines	MCDFN (Jahin et al., 2024)
Explainability/Interpretability	Methods (e.g., SHAP, PFI), model explanations, visualizations	MCDFN (Jahin et al., 2024)
Ethical Considerations	Bias, fairness, generalization, known failure modes	MCDFN (Jahin et al., 2024)
Implementation and Deployment	Software/hardware, integration, hyperparameters, retraining guidance	MCDFN (Jahin et al., 2024)
Future Work	Research and engineering directions	MCDFN (Jahin et al., 2024)

A unified model card should include all relevant formulas used for loss and metrics—such as $\mathrm{MSE}$, $\mathrm{MAE}$, Theil’s U, domain-aligned losses, and statistical tests—verbatim as in the underlying experimental study.

3. Methodological Rigor and Comparative Evaluation

Model cards ensure thoroughness in model evaluation. The standard practice includes:

• Defining all metrics in LaTeX (e.g., $$\mathrm{MSE} = \frac{1}{n}\sum_{i=1}^n (y_i - \hat y_i)^2$$).
• Reporting both absolute and relative model performance against competitive baselines, e.g., MCDFN’s comparison to BiLSTM, CNN, RNN, and other deep learning variants (Jahin et al., 2024).
• Using robust validation: sequential splits to preserve temporal causality, cross-validation for statistical significance (e.g., 10-fold paired $t$-tests for MCDFN), and reporting $p$-values.
• Benchmarking on public datasets for generalizability (e.g., FPN-fusion’s coverage of standard forecasting datasets such as ETTm1/ETTm2, Traffic, Weather (Li et al., 2024)).

Model cards also document data-specific preprocessing, including cyclic encoding, standardization procedures fitted to training splits, and special treatments for missing data or extreme values.

4. Explainability and Transparency

Modern forecasting model cards document both intrinsic and post-hoc explainability. Techniques may include:

• SHAP and ShapTime for time-series attribution (e.g., importance of forecast "super-times" in MCDFN (Jahin et al., 2024))
• Permutation Feature Importance (PFI) for feature influence ranking
• Sensitivity analyses, e.g., feature permutation or ablation

Explainability is embedded both for technical transparency (model validation, drift monitoring) and model trust in business contexts (explainable scorecards, trend attribution narratives driven by LLMs (Venkatachalam, 1 Oct 2025)). Cards often provide examples and visual artifacts—such as PFI bar-plots or attributions heatmaps.

5. Deployment Considerations, Robustness, and Limitations

Forecasting model cards specify all necessary information for reliable deployment:

• Codebase, framework, and hardware (e.g., TensorFlow 2.x/Keras, GPU/CPU requirements in MCDFN (Jahin et al., 2024); PyTorch environments in foundation models (Zhu et al., 27 Aug 2025))
• Hyperparameters used (e.g., CNN filter sizes, LSTM units, dropout rates for deep networks; changepoint scales for Prophet-based systems (Pillai et al., 14 Sep 2025))
• Concept drift monitoring practices (sliding window inference, retraining triggers, monitoring metrics such as MAPE/WMAPE bands)
• Known limitations and failure modes (data sparsity thresholds, issues with unseen exogenous shocks, outlier sensitivity)
• Scalability and maintenance strategies (model pruning/distillation, cluster-aware segmentation (Venkatachalam, 1 Oct 2025))

Model cards also address ethical use, responsible reporting, and calibration strategies to ensure reliability in dynamic real-world settings.

6. Extensions and Emerging Directions

The forecasting model card concept evolves to cover emergent trends:

• Foundation model documentation: Large-scale, domain-adaptive models (e.g., FinCast (Zhu et al., 27 Aug 2025)) include detailed coverage of architecture, training objectives (point-quantile loss, trend consistency), data diversity, and zero-shot/finetuned evaluation paradigms.
• Automated and interactive reporting: Model cards increasingly support integration with LLM-driven reporting pipelines, enabling deterministic, role-tailored audit artifacts and explainable business narratives (Venkatachalam, 1 Oct 2025).
• Scalable interfaces and adaptive analytics: Card templates now contemplate future enhancements such as dynamic dashboards, anomaly-aware monitoring, attention-based architecture augmentations (e.g., integration with Temporal Fusion Transformers).

7. Significance and Best Practices

Forecasting model cards underpin reproducibility, regulatory compliance, and operational trust in domains where forecasts directly affect planning, resource allocation, or policy. Their standardized, factual format supports model comparison, continuous validation, and risk mitigation. Best practice guidelines include:

• Maintain full metric and process transparency.
• Document dataset lineage, splits, and all engineering choices impacting generalization.
• Establish explicit boundaries of model validity and retrain schedules.
• Integrate explainability methods and communicate limitations clearly.

Forecasting model cards thus serve as both scientific documentation and practical deployment blueprints, driving robust forecasting outcomes across application domains (Jahin et al., 2024, Novak et al., 2017, Venkatachalam, 1 Oct 2025, Arab et al., 5 Feb 2025, Li et al., 2024, Zhu et al., 27 Aug 2025, Xue et al., 2023).