<p>Results of ARIMAX.</p>

Hamed Tabesh published a dataset on 2026-04-30 containing results from a study forecasting daily Emergency Department patient arrivals. The data likely includes performance metrics for ARIMA, ANN, LSTM, GLM, and two hybrid forecasting algorithms, incorporating meteorological and calendar influences. The dataset is 5.5 KB in size and is available in XLS format under a CC-BY-4.0 license.

Use Cases

• Benchmarking hybrid ARIMA-ANN forecasting algorithms based on the reported RMSE, ME, and SMAPE metrics.
• Analyzing the impact of meteorological and calendar factors on hospital patient flow as described in the study.
• Comparing model performance across short, intermediate, and long forecasting horizons as evaluated in the research.

Strengths

• Includes specific performance metrics (e.g., RMSE=33.82 for hybrid2) for multiple forecasting algorithms, enabling direct comparison.
• The study design incorporates both linear and nonlinear modeling techniques (ARIMA and ANN) to handle complex relationships in the data.
• Dataset is openly licensed under CC-BY-4.0, facilitating reuse and sharing.

Limitations

• Row count is unknown, which may limit suitability assessment for model training or validation.
• Column-level documentation is absent; field semantics must be inferred after download.
• The 5.5 KB file size suggests the dataset contains summary results or a small sample, not the underlying time-series data used for the forecasts.

Provenance

Source

Hamed Tabesh via figshare

Collection Method

Results from a research study developing hybrid forecasting algorithms for Emergency Department arrivals.

Freshness

Last updated 2026-04-30 17:47:38; freshness should be verified.