---
license: mit
task_categories:
  - time-series-forecasting
  - tabular-regression
language:
  - en
tags:
  - retail
  - demand-forecasting
  - gcc
  - e-commerce
  - gulf-retail
  - synthetic
  - agentic-commerce
  - ocg-dubai
size_categories:
  - 100K<n<1M
---

# GCC Retail Demand Forecasting Dataset v2

> Built by [OCG Dubai](https://ocg-dubai.ae) — Agentic Commerce APIs for the GCC

## Dataset Description

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.

### Dataset Summary

- **Total Records:** 210,384
- **Date Range:** 2018-01-01 to 2025-12-31 (8 years)
- **Countries:** UAE, KSA, Qatar, Kuwait, Bahrain, Oman
- **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)

### Features

| Feature | Type | Description |
|---------|------|-------------|
| date | string | Gregorian date (YYYY-MM-DD) |
| country | string | GCC country |
| category | string | Product category (12 types) |
| demand_index | float | Normalized demand (0-100) |
| temperature | float | Temperature in Celsius |
| day_of_week | int | Day of week (0=Monday) |
| month | int | Gregorian month |
| year | int | Gregorian year |
| is_weekend | bool | GCC weekend (Friday/Saturday) |
| is_ramadan | bool | Ramadan period |
| ramadan_week | int | Week within Ramadan (0-4) |
| is_eid_fitr | bool | Eid al-Fitr period |
| is_eid_adha | bool | Eid al-Adha period |
| is_shopping_festival | bool | Regional shopping festival |
| is_white_friday | bool | White Friday / Black Friday sales |
| is_national_day | bool | Country national day |
| is_back_to_school | bool | Back-to-school season |

### Product Categories

Based on actual GCC e-commerce revenue data:

| Category | Revenue Share | Notes |
|----------|--------------|-------|
| fashion_apparel | 25-38% | Largest across GCC |
| electronics_media | 19-34% | Second largest |
| groceries_fmcg | 15-30% | Fastest growing |
| beauty_cosmetics | 5-10% | Social media driven |
| home_furniture | 3-8% | Steady demand |
| luxury_goods | 2-7% | Significant in UAE/Qatar |
| jewelry_watches | 3-5% | Strong in UAE/KSA |
| health_wellness | 2-3% | Growing post-COVID |
| food_dining | 3-4% | Major F&B market |
| sports_outdoor | 1-2% | Vision 2030 growth |
| toys_kids | 1-2% | Seasonal spikes |
| travel_entertainment | 1-2% | Tourism driven |

### Market Data

| Country | Market Size (USD) | Key Events |
|---------|------------------|------------|
| UAE | $114B | Dubai Shopping Festival, Dubai Summer Surprises |
| KSA | $161B | Riyadh Season, Saudi National Day |
| Qatar | $19.5B | Shop Qatar, Qatar National Day |
| Kuwait | $22.6B | Hala February |
| Bahrain | $8.5B | F1 Grand Prix, National Day |
| Oman | $12.0B | Khareef Festival, National Day |

### Demand Patterns

1. **Ramadan**: Progressive increase (20-100% above baseline), peaks in weeks 3-4
2. **Eid al-Fitr/Adha**: Demand spikes for fashion, gifts, food
3. **Shopping Festivals**: DSF (UAE Jan-Feb), Riyadh Season (Oct-Mar), White Friday (Nov)
4. **Back to School**: August-September boost for kids, electronics
5. **National Days**: Country-specific celebration spending
6. **Temperature**: Summer drives indoor shopping, winter boosts outdoor/tourism

### Usage

```python
from datasets import load_dataset

dataset = load_dataset("GencoDiv/gcc-ramadan-retail-patterns")
df = dataset["train"].to_pandas()

# Filter by country
uae_data = df[df["country"] == "UAE"]

# Ramadan analysis
ramadan_data = df[df["is_ramadan"] == True]
```

### Related

- **Model:** [GencoDiv/ramadan-demand-forecaster](https://huggingface.co/GencoDiv/ramadan-demand-forecaster) — XGBoost model trained on this dataset (R²=0.992)

## About OCG Dubai

[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.

- Website: [ocg-dubai.ae](https://ocg-dubai.ae)

### Citation

```bibtex
@dataset{gcc_retail_v2_2026,
  title={GCC Retail Demand Forecasting Dataset v2},
  author={OCG Dubai},
  year={2026},
  url={https://ocg-dubai.ae},
  note={Synthetic dataset based on GCC market research}
}
```