Fractional residual plot for model validation
A pairwise comparison is important to measure the goodness-of-fit of models. Error measurements are used for this purpose but it only limit to the value, thus a graph is used to help show the precision of the models. These two should show a tally result in order to defense the hypothesis correctly....
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UTM Press
2017
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/76726/1/Suhartono2017_FractionalResidualPlotforModel.pdf http://eprints.utm.my/id/eprint/76726/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85008151765&doi=10.11113%2fjt.v79.8421&partnerID=40&md5=8cd23f148bfbdf27164a3f1aed509ab7 |
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| Summary: | A pairwise comparison is important to measure the goodness-of-fit of models. Error measurements are used for this purpose but it only limit to the value, thus a graph is used to help show the precision of the models. These two should show a tally result in order to defense the hypothesis correctly. In this study, a fractional residual plot is proposed to help showing the precision of forecasts. This plot improvises the scale of the graph by changing the scale into decimal ranging from -1 to 1. The closer the point to 0 will indicate that forecast is robust and value closer to -1 or 1 will indicate that the forecast is poor. Two error measurements which are mean absolute error (MAE) and mean absolute percentage error (MAPE) and residual plot are used to justify the results and make comparison with the proposed fractional residual plot. Three difference data are used for this purpose and the results have shown that the fractional residual plot could give as much information as the residual plot but in an easier and meaningful way. In conclusion, the error plot is important in visualize the accurateness of the forecast. |
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