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DemandBench

A comprehensive benchmarking framework for demand forecasting with time series datasets.

🎯 Overview

DemandBench provides a unified interface for loading, preprocessing, and evaluating demand forecasting models across multiple real-world datasets. The framework is designed to facilitate reproducible research and fair comparisons between different forecasting approaches. 📈 Enhanced Collection: Now featuring 14 comprehensive datasets spanning retail, pharmacy, hotel, grocery, online commerce, and supply chain domains, with over 200 million data points for robust demand forecasting research.

📦 Repository Structure

DemandBench uses a multi-repository architecture to manage datasets of different sizes while staying within GitHub's limits:

Main Repository (51MB)

  • Small & Medium datasets (< 200MB): Bakery, KaggleHotelDemand, Pharmacy, Pharmacy2, FreshRetail50k, AustralianRetail, HierarchicalSales, CarParts, KaggleRohlik, Yaz, KaggleRossmann, OnlineRetail, KaggleOnlineRetail2, VN1, KaggleRetail, KaggleWalmartStoreSales, Fossil
  • Core framework and evaluation tools
  • Examples and documentation

Large Dataset Repositories

🚀 Quick Start

Installation

git clone https://github.com/a11to1n3/DemandBench.git
cd DemandBench
pip install -e .

Loading Small & Medium Datasets (Built-in)

from demandbench.datasets.loaders import (
    load_bakery, load_hoteldemand, load_pharmacy, load_pharmacy2,
    load_rohlik, load_yaz, load_rossmann, load_freshretail50k,
    load_onlineretail, load_onlineretail2, load_australianretail, load_hierarchicalsales, load_carparts,
    load_vn1, load_kaggleretail, load_fossil
)

# Load small datasets (included in main repository)
dataset = load_bakery()
print(f"Features: {dataset.features.shape}")
print(f"Targets: {dataset.targets.shape}")

# Load medium datasets with rich metadata
rohlik = load_rohlik()                # 5M rows, Kaggle online grocery challenge (CZ)
rossmann = load_rossmann()            # 2M+ rows, Kaggle drugstore challenge (DE)
freshretail = load_freshretail50k()   # 4.5M rows, modern grocery benchmark (Asia)
pharmacy2 = load_pharmacy2()          # 279K rows, Southeast Asia pharmacy marketplace
onlineretail = load_onlineretail()    # 305K rows, UCI Online Retail dataset (UK)
onlineretail2 = load_onlineretail2()  # 1.8M+ rows, Kaggle Online Retail II dataset (UK)
australian = load_australianretail()  # 64K rows, Australian state/industry turnover
hierarchical = load_hierarchicalsales()  # 212K rows, pasta brand hierarchy (EU)
carparts = load_carparts()            # 136K rows, intermittent car parts demand
fossil = load_fossil()                # 45K+ rows, US fashion accessories

print(f"KaggleRohlik: {rohlik.features.shape}")
print(f"KaggleRossmann: {rossmann.features.shape}")
print(f"FreshRetail50k: {freshretail.features.shape}")
print(f"Pharmacy2: {pharmacy2.features.shape}")
print(f"OnlineRetail: {onlineretail.features.shape}")
print(f"KaggleOnlineRetail2: {onlineretail2.features.shape}")
print(f"AustralianRetail: {australian.features.shape}")
print(f"HierarchicalSales: {hierarchical.features.shape}")
print(f"CarParts: {carparts.features.shape}")
print(f"Fossil: {fossil.features.shape}")

Demand Pattern Lookup

Quickly retrieve representative series for Croston-style demand classes:

from demandbench.datasets.metadata import get_time_series_ids_by_demand_pattern
from demandbench.datasets.loaders import load_carparts

lumpy_ids = get_time_series_ids_by_demand_pattern("carparts", "lumpy", limit=3)
print("Sample lumpy series:", lumpy_ids)

carparts = load_carparts()
print("Metadata shortcut:", carparts.metadata.lumpy_ids[:3])

Loading Large Datasets (Separate Repositories)

Option 1: Clone alongside main repository

# Clone both repositories in the same directory
git clone https://github.com/a11to1n3/DemandBench.git
git clone https://github.com/a11to1n3/DemandBench-M5-Dataset.git
git clone https://github.com/a11to1n3/DemandBench-Favorita-Dataset.git

# Use normally
cd DemandBench
python -c "
from demandbench.datasets.loaders import load_m5
dataset = load_m5()  # Automatically finds sibling repository
print(f'Features: {dataset.features.shape}')
"

Option 2: Manual dataset integration

# Copy specific datasets to main repository
git clone https://github.com/a11to1n3/DemandBench-M5-Dataset.git
cp -r DemandBench-M5-Dataset/data/KaggleM5 DemandBench/demandbench/data/

📊 Available Datasets

Dataset Size Rows Features Period Source Region
Bakery 0.7 MB 127,575 15 2016-01-02→2019-04-30 Bakery sales Europe
KaggleHotelDemand 0.5 MB 46,508 9 2012-01-01→2020-10-31 Kaggle hotel demand Europe
Pharmacy 1.6 MB 54,621 44 2017-02-06→2019-05-13 African healthcare marketplace Africa
Pharmacy2 6.6 MB 279,330 16 2018-06-29→2020-05-12 Southeast Asia healthcare marketplace Asia
FreshRetail50k 76.3 MB 4,500,000 23 2024-03-28→2024-06-25 FreshRetail50k benchmark Asia
AustralianRetail 5.2 MB 64,532 8 1982-04-01→2018-12-01 Kaggle Australia retail turnover (CC BY-SA 4.0) Australia
HierarchicalSales 1.1 MB 212,164 9 2014-01-02→2018-12-31 Spiliotis et al. (2021) pasta hierarchy Europe
CarParts 0.5 MB 136,374 8 1998-01-01→2002-03-01 Zenodo car parts (CC BY 4.0) Australia
KaggleRohlik 26.2 MB 4,960,929 20 2020-08-01→2024-06-02 Kaggle Rohlik Challenge Europe
Yaz 0.1 MB 5,355 15 2013-10-04→2015-11-07 Yaz retail benchmark Generic
KaggleRossmann 9.7 MB 2,034,418 15 2013-01-01→2015-07-31 Kaggle Rossmann Drugstore Europe
OnlineRetail 24.3 MB 305,478 11 2010-12-01→2011-12-09 UCI Online Retail United Kingdom
KaggleOnlineRetail2 7.1 MB 1,883,152 14 2009-12-01→2011-12-09 Kaggle Online Retail II United Kingdom
KaggleDemand 1.1 MB 150,150 13 2011-01-17→2013-07-09 Kaggle Demand Forecasting United States
ProductDemand 2.2 MB 634,553 9 2011-01-08→2017-01-09 Kaggle Historical Product Demand (GPL-2.0) Global
VN1 9.5 MB 2,559,010 13 2020-07-06→2023-10-02 VN Group Supply Chain Europe
KaggleRetail 3.0 MB 421,570 20 2010-02-05→2012-10-26 Kaggle Retail Analytics United States
KaggleWalmartStoreSales 2.1 MB 421,570 20 2010-02-05→2012-10-26 Kaggle Walmart Recruiting competition United States
Fossil 1.3 MB 44,907 50 2016-01-01→2021-10-01 Fossil Group Sales United States
KaggleM5 223.7 MB 59,181,090 47 2011-01-29→2016-05-22 Kaggle M5 Forecasting United States
KaggleFavorita 1,138.9 MB 125,497,040 25 2013-01-01→2017-08-15 Kaggle Corporación Favorita Ecuador

🏢 = Requires separate repository

Key Features:

  • Unified schema with target, date, timeSeriesID, and metadata-backed feature splits
  • Parquet format with optimized compression (60-70% size reduction from original formats)
  • Rich metadata with detailed feature descriptions and domain-specific categorization
  • Geographic diversity spanning 6+ countries and multiple business domains
  • Supply chain complexity with multi-level client-warehouse-product structures (VN1)
  • Hierarchical promotion-aware sales via the HierarchicalSales pasta benchmark (Spiliotis et al., 2021)

🔧 System Requirements

For Small Datasets (≤ ~1 GB)

  • RAM: 1-2GB
  • Storage: 500MB free space

For Medium Datasets (~1–200 GB)

  • RAM: 2-6GB (4GB+ recommended)
  • Storage: 1GB free space

For Large Datasets (> 200 GB)

  • RAM: 12-24GB (16GB+ recommended)
  • Storage: 3-5GB free space
  • CPU: Multi-core recommended for faster loading

📈 Example Usage

After loading a dataset, you can aggregate over time or hierarchy before further processing:

weekly = bakery.aggregate_frequency("weekly")
store_rollup = bakery.aggregate_hierarchy("store")
import polars as pl
from demandbench.datasets.loaders import (
    load_australianretail,
    load_bakery,
    load_freshretail50k,
    load_hierarchicalsales,
    load_hoteldemand,
    load_kaggledemand,
    load_kaggleretail,
    load_kagglewalmart,
    load_m5,
    load_onlineretail,
    load_onlineretail2,
    load_pharmacy,
    load_pharmacy2,
    load_productdemand,
    load_rohlik,
    load_rossmann,
    load_vn1,
    load_yaz,
    load_fossil,
)

# Work with small datasets
bakery = load_bakery()
merged_data = bakery.get_merged_data()
print(f"Bakery data: {merged_data.shape}")

# Work with medium datasets (1M+ rows)
rohlik = load_rohlik()  # Kaggle online grocery challenge (CZ)
rossmann = load_rossmann()  # Kaggle drugstore challenge (DE)
onlineretail2 = load_onlineretail2()  # UK online retail (Online Retail II)
vn1 = load_vn1()  # Supply chain competition data
kaggleretail = load_kaggleretail()
fossil = load_fossil()  # US fashion accessories

# Compare datasets across regions
for name, loader in [("KaggleRohlik", load_rohlik), ("KaggleRossmann", load_rossmann), ("FreshRetail50k", load_freshretail50k), ("Pharmacy2", load_pharmacy2), ("OnlineRetail", load_onlineretail), ("KaggleOnlineRetail2", load_onlineretail2), ("HierarchicalSales", load_hierarchicalsales), ("VN1", load_vn1), ("KaggleRetail", load_kaggleretail), ("Fossil", load_fossil)]:
    dataset = loader()
    print(f"{name}: {dataset.features.shape[0]:,} rows, {dataset.features.shape[1]} features")
    
    # Check for lag_target_1 feature
    if "lag_target_1" in dataset.features.columns:
        print(f"  - Contains lag_target_1 feature ✓")

# Work with large dataset (if available)
try:
    m5 = load_m5()
    print(f"KaggleM5 data: {m5.features.shape}")
except FileNotFoundError as e:
    print("KaggleM5 dataset not found. Please clone the M5 repository.")
    print(e)

📋 Predefined Forecasting Tasks

Dataset Hierarchy Level Frequency Level Forecast Horizon
m5 product weekly 4
m5 product monthly 3
m5 store daily 7
favorita product weekly 4
favorita product monthly 3
favorita store daily 7
favorita store weekly 4
rohlik product/store weekly 4
rohlik product daily 7
rohlik product weekly 4
rossmann product/store weekly 4
rossmann store weekly 4
bakery product/store daily 7
bakery product/store weekly 4
bakery product daily 7
bakery store daily 7
bakery store weekly 4
yaz product daily 7
pharmacy product weekly 4
pharmacy2 product/store daily 7
pharmacy2 product/store weekly 4
freshretail50k product daily 7
freshretail50k store daily 7
hoteldemand product/store daily 7
hoteldemand product/store weekly 4
hoteldemand product daily 7
hoteldemand store daily 7
hoteldemand store weekly 4
onlineretail product weekly 4
onlineretail2 product weekly 4
australianretail product/store monthly 3
australianretail product monthly 3
australianretail store monthly 3
kaggledemand product/store weekly 4
kaggledemand store weekly 4
productdemand product/store weekly 4
productdemand product/store monthly 3
productdemand product weekly 4
productdemand product monthly 3
vn1 product weekly 4
kaggleretail product/store weekly 4
kaggleretail product weekly 4
kaggleretail store weekly 4
kagglewalmart store weekly 4
hierarchicalsales product daily 7
hierarchicalsales product weekly 4
hierarchicalsales product monthly 3
carparts product monthly 3
fossil product monthly 3

🛠️ Technical Features

  • 13 diverse datasets spanning retail, pharmacy, hotel, grocery, fashion, online commerce and supply chain
  • Automatic dataset detection across repositories
  • Memory-efficient chunk loading for large datasets
  • Advanced compression (parquet format) for optimal storage
  • Rich metadata with domain-specific feature categorization
  • Unified API regardless of dataset size or location
  • Flexible frequency aggregation and preprocessing
  • Built-in hierarchy aggregation to roll up by store or product
  • Forecast horizon utilities for backtesting and evaluation
  • Cross-validation utilities for time series
  • Geographic diversity with data from 6+ countries

📚 Examples

Explore the examples/ directory for complete usage patterns:

  • daily_linear_regression.py - Basic forecasting pipeline
  • weekly_random_forest.py - Feature-based forecasting
  • monthly_gradient_boosting.py - Advanced ML approaches
  • time_series_cross_validation.py - Model evaluation

🔄 Dataset Loading Logic

DemandBench automatically searches for datasets in this order:

  1. Sibling repositories (for KaggleM5, KaggleFavorita)

    • ../DemandBench-M5-Dataset/data/KaggleM5/
    • ../DemandBench-Favorita-Dataset/data/KaggleFavorita/

  2. Main repository (all datasets)

    • demandbench/data/{Dataset}/

  3. Format priority: Parquet → H5 → Feather → Zip extraction

Dataset Locations:

  • Built-in datasets: Bakery, KaggleHotelDemand, Pharmacy, Pharmacy2, FreshRetail50k, AustralianRetail, HierarchicalSales, CarParts, KaggleRohlik, Yaz, KaggleRossmann, OnlineRetail, KaggleOnlineRetail2, VN1, KaggleRetail, KaggleWalmartStoreSales, Fossil
  • External datasets: KaggleM5, KaggleFavorita (require separate repository cloning)

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Add tests and examples
  5. Submit a pull request

🔗 Related Repositories

🎯 Philosophy: One framework, 21 datasets, seamless experience - from small bakery sales to massive retail chains across multiple continents.

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Demand Time Series Forecasting Collection of Datasets

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