Middle and long-term prediction of UT1-UTC based on combination of Gray Model and Autoregressive Integrated Moving Average

2017 ◽  
Vol 59 (3) ◽  
pp. 888-894 ◽  
Author(s):  
Song Jia ◽  
Tian-he Xu ◽  
Zhang-zhen Sun ◽  
Jia-jing Li
Keyword(s):  
Moving Average ◽  
Gray Model ◽  
Autoregressive Integrated Moving Average ◽  
Term Prediction ◽  
Long Term Prediction

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.

scholarly journals Integrated Predictor Based on Decomposition Mechanism for PM2.5 Long-Term Prediction

2019 ◽  
Vol 9 (21) ◽  
pp. 4533 ◽  
Author(s):  
Xuebo Jin ◽  
Nianxiang Yang ◽  
Xiaoyi Wang ◽  
Yuting Bai ◽  
Tingli Su ◽  
...  
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Original Data ◽  
Multiple Components ◽  
Term Prediction ◽  
Gated Recurrent Units ◽  
Different Characteristics ◽  
Long Term Prediction ◽  
Beijing China

It is crucial to predict PM2.5 concentration for early warning regarding and the control of air pollution. However, accurate PM2.5 prediction has been challenging, especially in long-term prediction. PM2.5 monitoring data comprise a complex time series that contains multiple components with different characteristics; therefore, it is difficult to obtain an accurate prediction by a single model. In this study, an integrated predictor is proposed, in which the original data are decomposed into three components, that is, trend, period, and residual components, and then different sub-predictors including autoregressive integrated moving average (ARIMA) and two gated recurrent units are used to separately predict the different components. Finally, all the predictions from the sub-predictors are combined in fusion node to obtain the final prediction for the original data. The results of predicting the PM2.5 time series for Beijing, China showed that the proposed predictor can effectively improve prediction accuracy for 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.

scholarly journals Novel robust time series analysis for long-term and short-term prediction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroshi Okamura ◽  
Yutaka Osada ◽  
Shota Nishijima ◽  
Shinto Eguchi
Keyword(s):  
Robust Regression ◽  
Simulated Data ◽  
New Method ◽  
Short Term ◽  
Least Absolute Deviations ◽  
Term Prediction ◽  
Regression Approach ◽  
Short Term Prediction ◽  
Long Term Prediction

AbstractNonlinear phenomena are universal in ecology. However, their inference and prediction are generally difficult because of autocorrelation and outliers. A traditional least squares method for parameter estimation is capable of improving short-term prediction by estimating autocorrelation, whereas it has weakness to outliers and consequently worse long-term prediction. In contrast, a traditional robust regression approach, such as the least absolute deviations method, alleviates the influence of outliers and has potentially better long-term prediction, whereas it makes accurately estimating autocorrelation difficult and possibly leads to worse short-term prediction. We propose a new robust regression approach that estimates autocorrelation accurately and reduces the influence of outliers. We then compare the new method with the conventional least squares and least absolute deviations methods by using simulated data and real ecological data. Simulations and analysis of real data demonstrate that the new method generally has better long-term and short-term prediction ability for nonlinear estimation problems using spawner–recruitment data. The new method provides nearly unbiased autocorrelation even for highly contaminated simulated data with extreme outliers, whereas other methods fail to estimate autocorrelation accurately.

Long term prediction of forest resources

1985 ◽  
Vol 12 ◽  
pp. 176
Author(s):  
H Kurth ◽  
B Anders ◽  
G Lucas
Keyword(s):  
Forest Resources ◽  
Term Prediction ◽  
Long Term Prediction
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