---
id: "hospital-admissions-1d"
slug: "hospital-admissions-1d"
name: "Hospital Emergency Admissions"
domain: "Healthcare & Epidemiology"
taskType: "time_series_forecasting"
numRows: 13846
numColumns: 3
targetColumn: "target"
primaryMetric: "Timestral-123M SQL"
primaryScore: "0.5541"
parquetUrl: "https://huggingface.co/datasets/carlahq/demo-timeseries-benchmark-containers/resolve/main/containers/hospital-admissions-1d/dataset.parquet"
spreadsheetUrl: "https://docs.google.com/spreadsheets/d/19EJb4G_m8boFCahUsyh-hOTRzLmj7N5mXjdLhVNo-Kg/edit?usp=sharing"
canonicalUrl: "https://carlahq.eu/datasets/hospital-admissions-1d"
---

# Dataset: Hospital Emergency Admissions (hospital-admissions-1d)

> Benchmark of 8 daily time series tracking emergency department patient admissions across hospital facilities, evaluated over a 28-day forecasting horizon for clinical bed allocation and staff scheduling.

## Dataset Overview

| Attribute | Value |
| :--- | :--- |
| **Task Type** | `time_series_forecasting` |
| **Business Domain** | Healthcare & Epidemiology |
| **Target Column** | `target` |
| **Rows (Instances)** | 13,846 |
| **Features (Columns)** | 3 |
| **Primary Evaluation Metric** | Timestral-123M SQL |
| **Best Benchmark Score** | **0.5541** |
| **Prediction Horizon** | 28 steps |
| **Seasonality** | 7 |
| **Time Frequency** | Daily |
| **Timestamp Column** | `timestamp` |
| **Entity / ID Column** | `id` |

## Business Decision Context
**Context:** Hospital operational planning operates on weekly shift scheduling and 4-week bed capacity cycles. Operational directors and chief medical officers require forward visibility into emergency intake volumes to align nursing rosters, on-call physician shifts, ICU surge capacity, and elective surgery scheduling.
**Objective:** Accurately forecast daily emergency admissions across hospital facilities over a 28-day (4-week) horizon to optimize bed availability and staff staffing levels.
**Decision Impact:** Under-forecasting leads to severe emergency room overcrowding, extended triage wait times, ambulance diversions, and nurse burnout. Over-forecasting incurs high agency staffing premiums and underutilized staffed beds, adding thousands of euros per day in unnecessary clinical overhead.

## Feature Schema
| Column Name | Data Type | Role | Distinct Values | Missing Values (%) | Description |
| :--- | :--- | :--- | :--- | :--- | :--- |
| `id` | `categorical` | identifier | 8 | 0 | Unique identifier for the hospital facility or clinical department recording daily emergency admissions. |
| `timestamp` | `datetime` | timestamp | 1,731 | 0 | Daily calendar date corresponding to the emergency department patient intake recording window. |
| `target` | `numeric` | target | 26 | 0 | Total count of daily emergency department patient admissions requiring inpatient boarding or clinical triage. |

## 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/hospital-admissions-1d/dataset.parquet)
- **Google Sheets Interactive Demo:** [Open in Google Sheets](https://docs.google.com/spreadsheets/d/19EJb4G_m8boFCahUsyh-hOTRzLmj7N5mXjdLhVNo-Kg/edit?usp=sharing)
- **Academic Paper / Citation:** [Read Research Paper](https://arxiv.org/abs/2105.06643)
- **Canonical HTML Page:** [https://carlahq.eu/datasets/hospital-admissions-1d](https://carlahq.eu/datasets/hospital-admissions-1d)