Short-term prediction models for server management in Internet-based contexts

Unplanned events present significant challenges for operations and management in metro systems. Short-term ridership prediction can help agencies to better design contingency strategies under unplanned events. Though many short-term prediction methods have been proposed in the literature, most studies focused on typical situations or planned events. The study develops methods for the short-term metro ridership prediction under unplanned events. It explores event impact representation mechanisms and deals with the imbalanced data training problem in building the prediction model under unplanned events. Typical machine learning and deep learning methods are developed for exploration. A large-scale automatic fare collection (AFC) dataset and event record data for a heavily used metro system are used for empirical studies. The analysis found that the same type of unplanned event shares a similar and consistent demand change pattern (with respect to the demand under typical situations) at the station level. The synthetic minority oversampling technique (SMOTE) can enrich the ridership observations under unplanned events and generate a balanced dataset for model training. Given the occurrence of unplanned events, the results show that a combination of demand change ratio and the SMOTE oversampling technique enables the prediction models to learn the impact of unplanned events and improve the prediction accuracy under unplanned events. However, the oversampling methods (i.e., SMOTE and replication) slightly deteriorate the prediction accuracy for ridership under normal conditions. The findings provide insights into mechanisms for disruption impact representation and oversampling imbalanced data in model training, and guide the development of models for short-term prediction under unplanned events.

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Short-term prediction models for wind speed and wind power

The 2014 2nd International Conference on Systems and Informatics (ICSAI 2014) ◽

10.1109/icsai.2014.7009282 ◽

2014 ◽

Cited By ~ 1

Author(s):

Guangxing Bai ◽

Yanwu Ding ◽

Mehmet Bayram Yildirim ◽

Yan-Hong Ding

Keyword(s):

Wind Speed ◽

Wind Power ◽

Prediction Models ◽

Short Term ◽

Term Prediction ◽

Short Term Prediction

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The Use of Stochastic Models for Short-Term Prediction of Water Parameters of the Thesaurus Dam, River Nestos, Greece

Proceedings ◽

10.3390/proceedings2110634 ◽

2018 ◽

Vol 2 (11) ◽

pp. 634 ◽

Cited By ~ 1

Author(s):

Antonis Sentas ◽

Lina Karamoutsou ◽

Nikos Charizopoulos ◽

Thomas Psilovikos ◽

Aris Psilovikos ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Stochastic Models ◽

Prediction Models ◽

Short Term ◽

Predictive Capacity ◽

Water Parameters ◽

Term Prediction ◽

Artificial Neural ◽

Short Term Prediction

The scope of this paper is to evaluate the short-term predictive capacity of the stochastic models ARIMA, Transfer Function (TF) and Artificial Neural Networks for water parameters, specifically for 1, 2 and 3 steps forward (m = 1, 2 and 3). The comparison of statistical parameters indicated that ARIMA models could be proposed as short-term prediction models. In some cases that TF models resulted in better predictions, the difference with ARIMA was minimal and since the latter are simpler in their construction, they are proposed for short-term prediction. Artificial Neural Networks didn’t show a good short-term predictive capacity in comparison with the aforementioned models.

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