Combination forecasting method based on the fractal dimension weight

DOI

10.2991/aest-16.2016.127How to use a DOI?

Keywords

fractal dimension; unbiased grey forecasting model; SVM regression forecasting model; BP neural network forecasting model; combination forecasting model.

Abstract

In order to improve the prediction accuracy, a combined forecasting method based on fractal dimension weight is proposed in this paper. Firstly, since the amount of the original data will affect the accuracy of forecasting, the three spline interpolation method is used to increase the amount of data. Secondly, historical data fitting values is obtained by the unbiased grey forecasting model, the SVM regression forecasting model and the BP neural network model. According to these fitting values, the box dimension of every single forecasting model is calculated. The box dimension normalization results are taken as the weights of single forecasting model. Finally, the results of single forecasting models are combined by using the weighted average method. Verified by an example, the proposed combined forecasting method has higher accuracy than the single forecasting models.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Jiran Zhu
AU  - Yuancan Xu
AU  - Hua Leng
AU  - Haiguo Tang
AU  - Hanyang Gong
AU  - Zhidan Zhang
AU  - Pei Ao
PY  - 2016/11
DA  - 2016/11
TI  - Combination forecasting method based on the fractal dimension weight
BT  - Proceedings of the 2016 International Conference on Advanced Electronic Science and Technology (AEST 2016)
PB  - Atlantis Press
SP  - 960
EP  - 968
SN  - 1951-6851
UR  - https://doi.org/10.2991/aest-16.2016.127
DO  - 10.2991/aest-16.2016.127
ID  - Zhu2016/11
ER  -