Autoregressive integrated moving average model for long-term prediction of emergency department revenue and visitor volume

2011 ◽  
Author(s):  
Hon-Yi Shi ◽  
Jinn-Tsong Tsai ◽  
Wen-Hsien Ho ◽  
King-Teh Lee
Keyword(s):  
Emergency Department ◽  
Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Term Prediction ◽  
Moving Average Model ◽  
Long Term Prediction

State prediction for the actuators of civil aircraft based on a fusion framework of relevance vector machine and autoregressive integrated moving average

2018 ◽  
Vol 232 (5) ◽  
pp. 622-634 ◽  
Author(s):  
Runxia Guo ◽  
Jiaqi Wang ◽  
Na Zhang ◽  
Jiankang Dong
Keyword(s):  
Moving Average ◽  
Relevance Vector Machine ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
State Prediction ◽  
Civil Aircraft ◽  
Moving Average Model ◽  
Long Term Prediction ◽  
Fusion Framework

Relevance vector machine is a newly proposed and effective state prediction algorithm proved by practical applications; however, the accuracy of the single relevance vector machine model for the long-term prediction is unable to achieve satisfactory results with time goes by. Then, an autoregressive integrated moving average model is introduced to correct the prediction error caused by the single relevance vector machine, and a fusion framework based on the combination of relevance vector machine and autoregressive integrated moving average model is adopted to improve the accuracy of long-term prediction. In addition, a targeted approach for retraining the old model is put forward so that the state prediction model can be updated in time and suits the actual situation better. The effectiveness of the proposed fusion framework is illustrated via an aircraft actuator, and the experiments based on a model of civil aircraft actuator data set show that the proposed method yields a satisfied performance in state prediction of aircraft actuators.

Performance Similarity Based Method for Enhanced Prediction of Manufacturing Process Performance

2004 ◽  
Author(s):  
Jianbo Liu ◽  
Dragan Djurdjanovic ◽  
Jun Ni ◽  
Jay Lee
Keyword(s):  
Prediction Accuracy ◽  
Linear Prediction ◽  
Moving Average ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Term Prediction ◽  
Arma Modeling ◽  
Prediction Techniques ◽  
Long Term Prediction

Full realization of all potentials in predictive and proactive maintenance highly depends on the accuracy of long-term predictions of the remaining useful life of manufacturing equipment. Parametric linear prediction techniques, such as Autoregressive Moving Average modeling (ARMA), are routinely used to trend and predict future behavior of any time series, but are frequently not appropriate for long-term prediction because of the highly complicated and non-stationary nature of manufacturing processes. In this paper, we propose a novel method that is capable of achieving high long-term prediction accuracy by comparing signatures from two degradation processes using measures of similarity that form a Match Matrix. Through this concept, we can effectively include large amounts of historical information into the prediction of the current degradation process. Similarities with historical records are used to generate possible future distributions of features, which is then used to predict probabilities of failure over time by evaluating overlaps between predicted feature distributions and feature distributions related to unacceptable equipment behavior. Experimental results show that the proposed method results in a significant improvement of long-term prediction accuracy compared with ARMA modeling-based prediction.

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