---
id: "rossmann-1d"
slug: "rossmann-1d"
name: "Rossmann Store Daily & Weekly Sales"
domain: "Retail & Supply Chain"
taskType: "time_series_forecasting"
numRows: 1050330
numColumns: 19
targetColumn: "Sales"
primaryMetric: "Timestral-123M SQL"
primaryScore: "0.2690"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/rossmann-1d/dataset.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/demo_fev_rossmann-1d/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/rossmann-1d"
---

# Dataset: Rossmann Store Daily & Weekly Sales (rossmann-1d)

> Benchmark encompassing 1,115 German drugstore locations across daily intervals with a 48-day forecast horizon. Designed for retail demand planning, promo lift evaluation, and store-level revenue prediction.

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `time_series_forecasting` |
| **Business Domain** | Retail & Supply Chain |
| **Target Column** | `Sales` |
| **Rows (Instances)** | 1,050,330 |
| **Features (Columns)** | 19 |
| **Primary Evaluation Metric** | Timestral-123M SQL |
| **Best Benchmark Score** | **0.2690** |
| **Prediction Horizon** | 48 steps |
| **Seasonality** | 7 |
| **Time Frequency** | Daily |
| **Timestamp Column** | `timestamp` |
| **Entity / ID Column** | `id` |

## Business Decision Context
**Context:** Regional store managers and centralized replenishment planners execute daily replenishment cycles and rolling 6-to-7 week inventory commitments across 1,115 chemist/drugstore locations subject to fluctuating consumer footfall, Sunday closures, state-specific school holidays, and marketing promo schedules.
**Objective:** Generate accurate daily sales revenue forecasts per store across a 48-day forward horizon using known calendar promotions and store-specific metadata.
**Decision Impact:** Mitigates over-stocking perishable and fast-moving consumer goods (reducing working capital lockup and holding costs) while preventing costly out-of-stock events and lost gross margin during high-traffic promotional campaigns.

## Feature Schema
| Column Name | Data Type | Role | Distinct Values | Missing Values (%) | Description |
| :--- | :--- | :--- | :--- | :--- | :--- |
| `id` | `categorical` | identifier | 1,115 | 0 | Unique record or series identifier indexing individual store-level time series trajectories. |
| `timestamp` | `datetime` | timestamp | 942 | 0 | Daily calendar date corresponding to recorded sales and operational telemetry. |
| `Sales` | `numeric` | target | 21,734 | 33121 | Total turnover generated by the store on the specified day. |
| `DayOfWeek` | `numeric` | known_covariate | 7 | 0 | Calendar day of the week represented as an integer (1 = Monday, 7 = Sunday). |
| `Open` | `numeric` | known_covariate | 2 | 0 | Binary indicator specifying whether the store was open (1) or closed (0) for trade. |
| `Promo` | `numeric` | known_covariate | 2 | 0 | Indicator of whether a localized promotional discount campaign is active on that date. |
| `SchoolHoliday` | `numeric` | known_covariate | 2 | 0 | Indicator specifying whether public schools were closed on this date according to regional calendar. |
| `StateHoliday` | `categorical` | known_covariate | 4 | 0 | Categorical flag indicating state holidays (a = public holiday, b = Easter holiday, c = Christmas, 0 = None). |
| `Customers` | `numeric` | past_covariate | 4,086 | 33121 | Total customer headcount visiting the store on the day, observed historically. |
| `Assortment` | `categorical` | static_covariate | 3 | 0 | Store assortment depth classification (a = basic, b = extra, c = extended). |
| `CompetitionDistance` | `numeric` | static_covariate | 654 | 2826 | Straight-line physical distance in meters to the nearest competing retail store. |
| `CompetitionOpenSinceMonth` | `numeric` | static_covariate | 12 | 333468 | Approximate month when the nearest competitor opened for business. |
| `CompetitionOpenSinceYear` | `numeric` | static_covariate | 23 | 333468 | Year when the nearest competitor opened for business. |
| `Promo2` | `numeric` | static_covariate | 2 | 0 | Continuing and consecutive retail promotion participation flag (0 = not participating, 1 = participating). |
| `Promo2SinceWeek` | `numeric` | static_covariate | 24 | 512448 | Calendar week number when the store started participating in Promo2. |
| `Promo2SinceYear` | `numeric` | static_covariate | 7 | 512448 | Calendar year when the store began participating in Promo2. |
| `PromoInterval` | `categorical` | static_covariate | 3 | 512448 | Comma-separated consecutive schedule describing which months Promo2 restarts (e.g. 'Feb,May,Aug,Nov'). |
| `Store` | `numeric` | static_covariate | 1,115 | 0 | Unique numeric identifier assigned to each individual Rossmann outlet. |
| `StoreType` | `categorical` | static_covariate | 4 | 0 | Categorical store layout and footprint archetype (a, b, c, d). |

## 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/rossmann-1d/dataset.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/demo_fev_rossmann-1d/edit?usp=sharing)
- **Academic Paper / Citation:** [Read Research Paper](https://www.kaggle.com/c/rossmann-store-sales)
- **Canonical HTML Page:** [https://carlahq.eu/datasets/rossmann-1d](https://carlahq.eu/datasets/rossmann-1d)