动态多模型指数平滑法融合的在线预测方法

doi:10.3969/j.issn.1001-506X.2020.09.17

Abstract

Abstract:

To solve the problems of poor real-time for the traditional prediction algorithms, poor adaptability of the multiple data variations, and the parameters can't be adjusted online with real-time change in data, a method of real-time prediction based on fused by dynamic exponential smoothing of multi-model is proposed. In this algorithm, smoothing factors of exponential smoothing, fusion weights of single, quadratic and cubic exponential smoothing are adjusted dynamically based on the prediction error of historical data, and the sensors measuring parameters are real-time predicted accurately. The simulation shows that the algorithm is better than other exponential smoothing using independently in the case of multiple data variations.

Key words: online prediction, exponential smoothing, dynamic parameter, failure prediction, model fusion

CLC Number:

TP277
E917

Bofan YANG, Lin ZHANG, Bo ZHANG, Yafei SONG, Erqi DING. Method of online prediction based on fused by dynamic exponential smoothing of multi-model[J]. Systems Engineering and Electronics, 2020, 42(9): 2013-2021.

Figures/Tables 14

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References 33

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预测步数	1次平滑	2次平滑	3次平滑	ARIMA	本文算法
1步预测	0.186 0	0.169 5	0.324 5	0.739 5	0.169 7
2步预测	0.295 7	0.265 9	0.720 5	0.748 9	0.237 0
3步预测	0.402 0	0.369 7	1.273 3	0.749 9	0.316 7
4步预测	0.502 7	0.477 7	1.983 7	0.750 3	0.399 4
5步预测	0.594 9	0.573 6	2.849 6	0.756 0	0.475 8

信号样式	1次平滑	2次平滑	3次平滑	ARIMA	本文算法
恒定电压	0.2	0.1	0.2	851.6	1.1
正弦波	0.2	0.1	0.1	969.5	0.9
矩形脉冲	0.2	0.1	0.1	698.4	0.9
锯齿波	0.2	0.1	0.1	727.5	0.9

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Method of online prediction based on fused by dynamic exponential smoothing of multi-model

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