---
id: "rohlik-orders-1d"
slug: "rohlik-orders-1d"
name: "Rohlik E-Grocery Daily Fulfillment Orders"
domain: "Retail & Supply Chain"
taskType: "time_series_forecasting"
numRows: 7710
numColumns: 17
targetColumn: "orders"
primaryMetric: "Timestral-123M SQL"
primaryScore: "0.9335"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/rohlik-orders-1d/dataset.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/1czXBda0Pl0VPRO15U8lwWEgg34-Svu4rxjU0KTyPjBQ/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/rohlik-orders-1d"
---

# Dataset: Rohlik E-Grocery Daily Fulfillment Orders (rohlik-orders-1d)

> Daily order fulfillment volumes across 7 metropolitan fulfillment centers operated by European e-grocery leader Rohlik Group, covering a 61-day forward forecast horizon. The primary objective is multi-site demand forecasting utilizing calendar events, local weather, and web telemetry to optimize warehouse shift capacity and supply chain replenishment.

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `time_series_forecasting` |
| **Business Domain** | Retail & Supply Chain |
| **Target Column** | `orders` |
| **Rows (Instances)** | 7,710 |
| **Features (Columns)** | 17 |
| **Primary Evaluation Metric** | Timestral-123M SQL |
| **Best Benchmark Score** | **0.9335** |
| **Prediction Horizon** | 61 steps |
| **Seasonality** | 7 |
| **Time Frequency** | Daily |
| **Timestamp Column** | `timestamp` |
| **Entity / ID Column** | `id` |

## Business Decision Context
**Context:** Rohlik operates rapid-delivery fulfillment centers across European metropolitan areas (including Prague, Brno, Munich, and Frankfurt). Warehouse managers and operations planners must schedule picker shifts, delivery driver fleets, and perishable stock procurement 1 to 61 days ahead to handle daily fluctuating grocery demand driven by weather and local calendar anomalies.
**Objective:** Predict daily fulfillment order counts (`orders`) for each warehouse location over a 61-day horizon H, accounting for known public holidays, school recesses, brick-and-mortar retail closures, and historical web telemetry.
**Decision Impact:** Under-forecasting leads to delivery slot throttling, severe last-mile delivery delays, and lost gross merchandise value (GMV). Over-forecasting creates excess labor costs from idle warehouse pickers and couriers, as well as acute food spoilage across short-shelf-life fresh grocery categories.

## Feature Schema
| Column Name | Data Type | Role | Distinct Values | Missing Values (%) | Description |
| :--- | :--- | :--- | :--- | :--- | :--- |
| `id` | `categorical` | identifier | 7 | 0 | Unique identifier for the metropolitan fulfillment warehouse center (e.g. Brno_1, Prague_1, Munich_1) |
| `timestamp` | `datetime` | timestamp | 1,197 | 0 | Calendar observation date formatted at daily granularity (YYYY-MM-DD) |
| `orders` | `numeric` | target | 4,426 | 370 | Total number of completed grocery fulfillment orders delivered on the given date (primary target variable) |
| `holiday` | `numeric` | known_covariate | 2 | 370 | Binary indicator specifying whether the date is a recognized public or bank holiday in the warehouse region |
| `school_holidays` | `numeric` | known_covariate | 2 | 370 | Binary indicator representing active primary and secondary school vacation periods in the regional market |
| `shops_closed` | `numeric` | known_covariate | 2 | 370 | Binary indicator reflecting mandatory physical retail store closure mandates (e.g., German Sunday trading bans or statutory holiday closures), driving shopping volume online |
| `winter_school_holidays` | `numeric` | known_covariate | 2 | 370 | Binary flag denoting regional mid-winter and spring break school holiday windows |
| `blackout` | `numeric` | past_covariate | 2 | 370 | Binary flag indicating an unscheduled facility power outage or major IT infrastructure disruption |
| `frankfurt_shutdown` | `numeric` | past_covariate | 2 | 370 | Binary indicator of localized operational stoppage or warehouse commissioning downtime at the Frankfurt distribution center |
| `mini_shutdown` | `numeric` | past_covariate | 2 | 370 | Binary flag indicating localized equipment, automation conveyor maintenance, or partial intra-day dispatch delays |
| `mov_change` | `numeric` | past_covariate | 3 | 370 | Indicator of revisions to minimum order value thresholds or delivery fee structure influencing basket sizing and transaction frequency |
| `precipitation` | `numeric` | past_covariate | 772 | 640 | Recorded rainfall or liquid equivalent precipitation depth in the metropolitan service area |
| `shutdown` | `numeric` | past_covariate | 2 | 370 | Binary flag indicating a complete suspension of warehouse order fulfillment operations |
| `snow` | `numeric` | past_covariate | 158 | 640 | Measured snowfall accumulation impacting courier travel speeds and customer stay-at-home ordering behavior |
| `user_activity_1` | `numeric` | past_covariate | 1,135 | 370 | Aggregated upstream digital search, app engagement, or active shopper session telemetry preceding order checkout |
| `user_activity_2` | `numeric` | past_covariate | 6,147 | 370 | High-volume web interaction and catalog browsing activity metric capturing real-time shopping intent |
| `holiday_name` | `categorical` | known_covariate | 24 | 7492 | Text descriptor of specific cultural, religious, or statutory holiday events (e.g. Christmas Eve, Easter Monday, New Year's Day) |

## FEV Benchmark Leaderboard (AutoGluon Time-Series Suite)
Empirical probabilistic evaluation across standard foundation models and baselines:

| Rank | Method / Model | Family | Organization | SQL Win Rate (%) | SQL Skill Score | MASE Win Rate (%) | Median Latency | Execution |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | **Timestral-209M** | Foundation Model | — | 87.7% | 50.39 | 86.0% | 0.51s | Server (CUDA / PyTorch) |
| 2 | **Timestral-123M (Carla Engine)** | Foundation Model | — | 86.8% | 50.18 | 85.0% | 0.32s | WebGPU (Browser / Sheets) |
| 3 | **TimeRSI** | Closed-Source API | — | 84.8% | 51.24 | 83.8% | 2512s | Server (CUDA / PyTorch) |
| 4 | **t0-gamma** | Foundation Model | — | 84.4% | 49.42 | 81.8% | 4.38s | Server (CUDA / PyTorch) |
| 5 | **Timestral-28M** | Foundation Model | — | 79.2% | 48.42 | 77.0% | 0.11s | Server (CUDA / PyTorch) |
| 6 | **timesfm-3** | Foundation Model | — | 78.5% | 48.99 | 77.0% | 3.41s | Server (CUDA / PyTorch) |
| 7 | **ChakraTS** | Baseline | — | 78.2% | 48.14 | 79.5% | 14.22s | Server (CUDA / PyTorch) |
| 8 | **Lingjiang2.0** | Baseline | — | 73.9% | 47.02 | 71.3% | 8.89s | Server (CUDA / PyTorch) |
| 9 | **Chronos-2** | Foundation Model | — | 70.4% | 47.28 | 68.4% | 0.84s | Server (CUDA / PyTorch) |
| 10 | **timeh2o-v1-tiny** | Foundation Model | — | 69.9% | 45.49 | 67.0% | 0.19s | Server (CUDA / PyTorch) |
| 11 | **Timestral-5M** | Foundation Model | — | 69.0% | 46.19 | 67.7% | 0.04s | Server (CUDA / PyTorch) |
| 12 | **Toto-2.0-2.5B** | Baseline | — | 67.5% | 44.42 | 65.7% | 5.58s | Server (CUDA / PyTorch) |
| 13 | **t0-beta** | Foundation Model | — | 67.4% | 46.65 | 64.5% | 2.29s | Server (CUDA / PyTorch) |
| 14 | **Toto-2.0-1B** | Baseline | — | 66.4% | 44.43 | 65.5% | 2.48s | Server (CUDA / PyTorch) |
| 15 | **TiRex-2** | Foundation Model | — | 65.8% | 45.50 | 63.4% | 0.27s | Server (CUDA / PyTorch) |

## Provenance & Downloadable Artifacts
- **Dataset Parquet File:** [Direct Parquet Download](https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/rohlik-orders-1d/dataset.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/1czXBda0Pl0VPRO15U8lwWEgg34-Svu4rxjU0KTyPjBQ/edit?usp=sharing)
- **Academic Paper / Citation:** [Read Research Paper](https://www.kaggle.com/competitions/rohlik-orders-forecasting-challenge)
- **Canonical HTML Page:** [https://carlahq.eu/datasets/rohlik-orders-1d](https://carlahq.eu/datasets/rohlik-orders-1d)