Combination Forecasting Algorithm Based on Non-Equal Interval Weighted Grey Model and Neural Network

HAN Jin; YANG Yue; CHEN Feng; LI Xiong-bin

doi:10.3879/j.issn.1000-0887.2013.04.009

Volume 34 Issue 4

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Article Navigation > Applied Mathematics and Mechanics > 2013 > 34(4): 408-419

HAN Jin, YANG Yue, CHEN Feng, LI Xiong-bin. Combination Forecasting Algorithm Based on Non-Equal Interval Weighted Grey Model and Neural Network[J]. Applied Mathematics and Mechanics, 2013, 34(4): 408-419. doi: 10.3879/j.issn.1000-0887.2013.04.009

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Combination Forecasting Algorithm Based on Non-Equal Interval Weighted Grey Model and Neural Network

doi: 10.3879/j.issn.1000-0887.2013.04.009

School of Traffic and Transportation Engineering, Central South University, Changsha 410075, P.R.China

Received Date: 2013-02-08
Rev Recd Date: 2013-03-25
Publish Date: 2013-04-15

Abstract

Abstract

The nonequal interval forecasting algorithm plays an important role in trend analysis and forecasting of sequences with different intervals. Based on the traditional grey forecasting theory, a combination forecasting algorithm based on nonequal interval weighted grey model and neural network was proposed. By constructing the nonequal interval weighted grey forecasting model, the average of original data sequence was regarded as the initial value of cumulative sequence, the integral area of continuous accumulation function was used as the background value, and the cumulative sequence was processed by weighting in order to truly reflect the impact of time sequences development to forecasting results. On this basis, BP neural network was introduced to correct the residuals sequence of grey forecasting which further improved the forecasting accuracy. The numerical example indicates that the forecasting accuracy level of the algorithm is 1 and higher than similar algorithms.
- forecasting,
- non-equal interval,
- grey model,
- weighted,
- neural network,
- residual modification

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References

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