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topprismdata (@topprismdata) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Unified Vs Day Specific Forecasting by topprismdata

Name: Unified Vs Day Specific Forecasting
Author: topprismdata

Unified Vs Day Specific Forecasting

Use when: (1) deciding between unified vs day-specific models for multi-step forecasting, (2) considering blending day-specific with unified predictions, (3) CV shows day-specific worse than unified but unsure if LB will agree, (4) building multi-step time series models with >7 day horizons. KEY FINDING: Unified model with target_day_offset feature can significantly outperform day-specific models, especially when features are rich enough to encode temporal patterns.

topprismdata 0d4242e 3.0 KB Updated Sep 17, 2026

File contents

Unified Model vs Day-Specific in Multi-Step Forecasting

The Assumption

Day-specific models (one per horizon day) should outperform a single unified model because:

Each model specializes in its specific horizon
No need for the model to "figure out" how day offset affects predictions
Each model has its full capacity for one task

The Finding

In Kaggle Favorita Store Sales (16-day horizon, 1782 store-family pairs):

Approach	CV RMSLE	LB
Day-specific (16 models)	0.42567	0.39779
Unified (1 model + target_day_offset)	0.38206	0.38850
Blend 70/30	—	0.39393
Blend 50/50	—	~0.391

Unified model is 0.00929 LB better than day-specific.

Why Unified Wins Here

More training data: Unified sees 16× more samples (46M vs 2.87M per model), giving LightGBM more statistical power to learn rare patterns.
Cross-day generalization: The unified model learns that "sales patterns on Monday" are similar whether predicting day 1 (if target is Monday) or day 8 (if target is Monday). Day-specific models can't share this knowledge.
Rich features encode temporal structure: With YoY, TE, and lag features, the model already has enough information to distinguish between horizons. The target_day_offset feature is sufficient for the model to specialize internally.
Day-specific overfits the fold structure: Each day-specific model trains on the same store-family pairs with very similar time splits, potentially overfitting to the CV fold boundaries.

When Day-Specific Might Still Win

Very long horizons (>30 days) where temporal patterns change dramatically
When features are minimal (model needs explicit specialization)
When different horizons have fundamentally different data availability

Rule of Thumb

Rich features + moderate horizon (7-30 days): Try unified first
Sparse features + long horizon: Day-specific may be better
Always test both and use controlled comparison (not just CV)

Evidence

Kaggle Store Sales (Favorita), April 2026:

R13 (day-specific + YoY): LB=0.39779
R14 (unified + YoY): LB=0.38850 (NEW BEST, -0.00929)
Blend actually WORSE than unified alone

yoy-364day-features — the features used in both approaches
ts-day-specific-forecasting — the day-specific approach (still valid in some contexts)
controlled-submission-experiment — methodology for comparing approaches

---name: unified-vs-day-specific-forecastingdescription: Use when: (1) deciding between unified vs day-specific models for multi-step forecasting, (2) considering blending day-specific with unified predictions, (3) CV shows day-specific worse than unified but unsure if LB will agree, (4) building multi-step time series models with >7 day horizons. KEY FINDING: Unified model with target_day_offset feature can significantly outperform day-specific models, especially when features are rich enough to encode temporal patterns.---# Unified Model vs Day-Specific in Multi-Step Forecasting## The AssumptionDay-specific models (one per horizon day) should outperform a single unified model because:- Each model specializes in its specific horizon- No need for the model to "figure out" how day offset affects predictions- Each model has its full capacity for one task## The FindingIn Kaggle Favorita Store Sales (16-day horizon, 1782 store-family pairs):| Approach | CV RMSLE | LB ||----------|----------|--------|| Day-specific (16 models) | 0.42567 | 0.39779 || Unified (1 model + target_day_offset) | 0.38206 | **0.38850** || Blend 70/30 | — | 0.39393 || Blend 50/50 | — | ~0.391 |**Unified model is 0.00929 LB better than day-specific.**## Why Unified Wins Here1. **More training data**: Unified sees 16× more samples (46M vs 2.87M per model), giving LightGBM more statistical power to learn rare patterns.2. **Cross-day generalization**: The unified model learns that "sales patterns on Monday" are similar whether predicting day 1 (if target is Monday) or day 8 (if target is Monday). Day-specific models can't share this knowledge.3. **Rich features encode temporal structure**: With YoY, TE, and lag features, the model already has enough information to distinguish between horizons. The `target_day_offset` feature is sufficient for the model to specialize internally.4. **Day-specific overfits the fold structure**: Each day-specific model trains on the same store-family pairs with very similar time splits, potentially overfitting to the CV fold boundaries.## When Day-Specific Might Still Win- Very long horizons (>30 days) where temporal patterns change dramatically- When features are minimal (model needs explicit specialization)- When different horizons have fundamentally different data availability## Rule of Thumb- **Rich features + moderate horizon (7-30 days)**: Try unified first- **Sparse features + long horizon**: Day-specific may be better- **Always test both** and use controlled comparison (not just CV)## EvidenceKaggle Store Sales (Favorita), April 2026:- R13 (day-specific + YoY): LB=0.39779- R14 (unified + YoY): LB=0.38850 (NEW BEST, -0.00929)- Blend actually WORSE than unified alone## Related- `yoy-364day-features` — the features used in both approaches- `ts-day-specific-forecasting` — the day-specific approach (still valid in some contexts)- `controlled-submission-experiment` — methodology for comparing approaches

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