A 70.7 KB dataset used to develop hybrid forecasting algorithms for Emergency Department patient arrivals. The data likely contains daily arrival counts influenced by meteorological and calendar factors, as described in a study by Hamed Tabesh. The dataset was last updated on April 30, 2026, and includes performance metrics for ARIMA, ANN, LSTM, GLM, and two hybrid models.
Use Cases
- Benchmarking forecasting algorithms like ARIMA and LSTM based on the reported RMSE, ME, and SMAPE metrics.
- Developing hybrid models for patient flow prediction based on the described combination of linear and nonlinear methodologies.
- Analyzing the impact of calendar and weather variables on hospital demand based on the factors mentioned in the description.
- Comparing forecast accuracy across short, intermediate, and long time horizons as evaluated in the source study.
Strengths
- Includes specific performance metrics (e.g., RMSE=33.82 for the best hybrid model) for multiple forecasting algorithms.
- The description details a clear methodological approach using hybrid ARIMA-ANN models.
- Dataset is openly licensed under CC-BY-4.0.
Limitations
- The dataset is very small at 70.7 KB, indicating limited scope.
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
Provenance
- Source
- Hamed Tabesh via figshare.
- Collection Method
- Data was likely collected for a study on forecasting Emergency Department arrivals, but the specific collection method is not detailed.
- Freshness
- Last updated 2026-04-30 17:47:27; freshness should be verified.