Combination forecasting method based on the fractal dimension weight
Jiran Zhu, Yuancan Xu, Hua Leng, Haiguo Tang, Hanyang Gong, Zhidan Zhang, Pei Ao
Available Online November 2016.
- https://doi.org/10.2991/aest-16.2016.127How to use a DOI?
- fractal dimension; unbiased grey forecasting model; SVM regression forecasting model; BP neural network forecasting model; combination forecasting model.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - 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 - https://doi.org/10.2991/aest-16.2016.127 ID - Zhu2016/11 ER -