Forecasting year train/test cutoff sensitivity analysis.

<p>The average within-city standardized test-set RMSE for TSIR and SFNN for all combinations of <i>k</i> forecasting windows ∈ {1, 4, 12, 20, 34, 52} and train/test cutoff years ∈ {1960, 1961, 1962} demonstrates that the improvement of the SFNN model over the TSIR is stable across train/test cutoff points.</p> <p>(PDF)</p>

Publication Details

Journal
Figshare
Published
2024-11-21
DOI
https://doi.org/10.1371/journal.pcbi.1012616.s002
Primary Topic
Traffic Prediction and Management Techniques
Type
article
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Forecasting year train/test cutoff sensitivity analysis.

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Traffic Prediction and Management Techniques
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Forecasting year train/test cutoff sensitivity analysis.

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Abstract

<p>The average within-city standardized test-set RMSE for TSIR and SFNN for all combinations of <i>k</i> forecasting windows ∈ {1, 4, 12, 20, 34, 52} and train/test cutoff years ∈ {1960, 1961, 1962} demonstrates that the improvement of the SFNN model over the TSIR is stable across train/test cutoff points.</p> <p>(PDF)</p>

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