2020
DOI: 10.1016/j.neucom.2019.06.109
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A freight inspection volume forecasting approach using an aggregation/disaggregation procedure, machine learning and ensemble models

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Cited by 10 publications
(10 citation statements)
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“…On the other hand, considering that current research emphasizes only time series and regression analysis, Moscoso-López et al [46] used a two-stage approach involving an ensemble of the best SVR models to forecast ro-ro (roll-on-roll-off) freight flow, which was verified as a promising tool in freight forecasting. For further analysis of the influence of the size of the autoregressive window and machine learning models, Ruiz-Aguilar et al [11] used a variety of well-known machine learning models (individual learners) and ensemble models to predict the number of inspections at the Border Inspection Posts (BIPs) on a specific day. According to previous studies, neural networks and support vector regression generally perform better than other machine learners in the forecast of freight volume.…”
Section: Forecasting Methods For Freight Volumementioning
confidence: 99%
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“…On the other hand, considering that current research emphasizes only time series and regression analysis, Moscoso-López et al [46] used a two-stage approach involving an ensemble of the best SVR models to forecast ro-ro (roll-on-roll-off) freight flow, which was verified as a promising tool in freight forecasting. For further analysis of the influence of the size of the autoregressive window and machine learning models, Ruiz-Aguilar et al [11] used a variety of well-known machine learning models (individual learners) and ensemble models to predict the number of inspections at the Border Inspection Posts (BIPs) on a specific day. According to previous studies, neural networks and support vector regression generally perform better than other machine learners in the forecast of freight volume.…”
Section: Forecasting Methods For Freight Volumementioning
confidence: 99%
“…The freight volumes displayed periodicity with day [12] and season [15] as the grain size, respectively. In addition, Ruiz-Aguilar et al [11] point out that although the prediction result will improve with the increasing size of the autoregressive window, the improvement will reduce with each increment of the size of the autoregressive window, up to a point of 21 days in the past, which implies that δ = 3 (weeks) may be a good choice. We verified this value through further experiments.…”
Section: Autocorrelation Analysismentioning
confidence: 99%
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