Research of combination forecasting model based on improved analytic hierarchy process

Abstract

The weighting method of the traditional fixed combination forecasting model is the only criterion considered to improve accuracy, which has some limitations. In order to improve the comprehensive prediction performance of the combined model, hierarchical structure of the combined model by selecting some parameters which can reflect the performance of the model (including prediction accuracy, robustness, sensitivity, and the amount of fitting data) is established and a kind of multiple factor and multiple criteria weighting method of combination forecasting model is put forward. Based on SVR model, GM (1, 1) model, and ARIMA model, a combination forecasting model based on Improved Analytic Hierarchy Process (AHP) is constructed and applied to a foundation pit. The experimental results show that the combined forecasting model based on improved AHP are better than the single model in precision and robustness; it also has good effect in sensitivity, which has more comprehensive prediction performance than the single models, and has good engineering and practical value.

Research on Deformation Prediction of Foundation Pit Based on PSO‐GM‐BP Model

“…e more common ones are the regression method, time series analysis model, gray system analysis model, Kalman filter model, artificial neural network model, spectrum analysis method, etc. [5][6][7][8][9][10].…”