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--- |
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license: mit |
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task_categories: |
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- time-series-forecasting |
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- tabular-regression |
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language: |
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- en |
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tags: |
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- retail |
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- demand-forecasting |
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- gcc |
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- e-commerce |
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- gulf-retail |
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- synthetic |
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- agentic-commerce |
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- ocg-dubai |
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size_categories: |
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- 100K<n<1M |
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--- |
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# GCC Retail Demand Forecasting Dataset v2 |
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> Built by [OCG Dubai](https://ocg-dubai.ae) — Agentic Commerce APIs for the GCC |
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## Dataset Description |
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A synthetic dataset for retail demand forecasting across 6 Gulf Cooperation Council (GCC) countries and 12 product categories. Based on real GCC market research with country-specific revenue shares, seasonal events, and shopping festivals. Gregorian calendar with event flags — no Hijri dependency. |
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### Dataset Summary |
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- **Total Records:** 210,384 |
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- **Date Range:** 2018-01-01 to 2025-12-31 (8 years) |
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- **Countries:** UAE, KSA, Qatar, Kuwait, Bahrain, Oman |
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- **Categories:** 12 (fashion_apparel, electronics_media, groceries_fmcg, beauty_cosmetics, home_furniture, luxury_goods, jewelry_watches, health_wellness, food_dining, sports_outdoor, toys_kids, travel_entertainment) |
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### Features |
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| Feature | Type | Description | |
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|---------|------|-------------| |
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| date | string | Gregorian date (YYYY-MM-DD) | |
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| country | string | GCC country | |
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| category | string | Product category (12 types) | |
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| demand_index | float | Normalized demand (0-100) | |
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| temperature | float | Temperature in Celsius | |
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| day_of_week | int | Day of week (0=Monday) | |
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| month | int | Gregorian month | |
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| year | int | Gregorian year | |
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| is_weekend | bool | GCC weekend (Friday/Saturday) | |
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| is_ramadan | bool | Ramadan period | |
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| ramadan_week | int | Week within Ramadan (0-4) | |
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| is_eid_fitr | bool | Eid al-Fitr period | |
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| is_eid_adha | bool | Eid al-Adha period | |
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| is_shopping_festival | bool | Regional shopping festival | |
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| is_white_friday | bool | White Friday / Black Friday sales | |
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| is_national_day | bool | Country national day | |
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| is_back_to_school | bool | Back-to-school season | |
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### Product Categories |
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Based on actual GCC e-commerce revenue data: |
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| Category | Revenue Share | Notes | |
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|----------|--------------|-------| |
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| fashion_apparel | 25-38% | Largest across GCC | |
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| electronics_media | 19-34% | Second largest | |
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| groceries_fmcg | 15-30% | Fastest growing | |
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| beauty_cosmetics | 5-10% | Social media driven | |
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| home_furniture | 3-8% | Steady demand | |
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| luxury_goods | 2-7% | Significant in UAE/Qatar | |
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| jewelry_watches | 3-5% | Strong in UAE/KSA | |
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| health_wellness | 2-3% | Growing post-COVID | |
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| food_dining | 3-4% | Major F&B market | |
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| sports_outdoor | 1-2% | Vision 2030 growth | |
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| toys_kids | 1-2% | Seasonal spikes | |
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| travel_entertainment | 1-2% | Tourism driven | |
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### Market Data |
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| Country | Market Size (USD) | Key Events | |
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|---------|------------------|------------| |
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| UAE | $114B | Dubai Shopping Festival, Dubai Summer Surprises | |
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| KSA | $161B | Riyadh Season, Saudi National Day | |
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| Qatar | $19.5B | Shop Qatar, Qatar National Day | |
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| Kuwait | $22.6B | Hala February | |
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| Bahrain | $8.5B | F1 Grand Prix, National Day | |
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| Oman | $12.0B | Khareef Festival, National Day | |
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### Demand Patterns |
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1. **Ramadan**: Progressive increase (20-100% above baseline), peaks in weeks 3-4 |
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2. **Eid al-Fitr/Adha**: Demand spikes for fashion, gifts, food |
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3. **Shopping Festivals**: DSF (UAE Jan-Feb), Riyadh Season (Oct-Mar), White Friday (Nov) |
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4. **Back to School**: August-September boost for kids, electronics |
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5. **National Days**: Country-specific celebration spending |
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6. **Temperature**: Summer drives indoor shopping, winter boosts outdoor/tourism |
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### Usage |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("GencoDiv/gcc-ramadan-retail-patterns") |
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df = dataset["train"].to_pandas() |
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# Filter by country |
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uae_data = df[df["country"] == "UAE"] |
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# Ramadan analysis |
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ramadan_data = df[df["is_ramadan"] == True] |
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``` |
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### Related |
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- **Model:** [GencoDiv/ramadan-demand-forecaster](https://huggingface.co/GencoDiv/ramadan-demand-forecaster) — XGBoost model trained on this dataset (R²=0.992) |
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## About OCG Dubai |
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[OCG Dubai](https://ocg-dubai.ae) builds Agentic Commerce APIs for the GCC market — demand forecasting, halal compliance, smart baskets, and dynamic pricing calibrated for regional consumer behavior. |
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- Website: [ocg-dubai.ae](https://ocg-dubai.ae) |
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### Citation |
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```bibtex |
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@dataset{gcc_retail_v2_2026, |
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title={GCC Retail Demand Forecasting Dataset v2}, |
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author={OCG Dubai}, |
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year={2026}, |
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url={https://ocg-dubai.ae}, |
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note={Synthetic dataset based on GCC market research} |
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} |
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``` |
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