Abstract
In view of the existing combination forecasting methods based on rough set theory that may not be able to weight individual individual forecasting models, the attribute importance in the original method is adjusted and combined with the root mean square error of the individual forecasting model to form a new attribute importance. The new attribute importance is used to determine the combination forecasting weight coefficients, which solves the problem that the original method cannot be weighted, and increases the consideration of forecasting accuracy. Weight coefficients are also determined according to the historical forecasting performance of the models, which reflects the forecasting stability of the models. The integrated weighting method is used to fuse the two kinds of weight coefficients. Based on a certain type of ship maintenance cost data example, the improved method is compared with the commonly used combined forecasting methods, and better results are obtained, the accuracy and stability of the forecasting are improved, and the effectiveness of the method is verified.
Highlights
In the forecasting of ship maintenance costs in of in China, due to the large time span of costs and many influencing factors, the collection of relevant data started late[13],using traditional cost estimation methods and individual forecasting models is difficult to describe its changing law[4].Bates and Granger[5] proposed the concept of combination forecasting in 1969
This paper analyzes the existing combination forecasting method based on the attribute importance of rough set theory, points out its problems in determining the weight coefficients, adjusts the importance of the original attribute, and characterizes the average forecasting accuracy of the individual forecasting model
The root square error is combined with it to form a new attribute importance for the determination of the combination forecasting weight coefficients, which solves the problem that the weight coefficients may not be determined in the original method, and makes the information contained in the new attribute importance more comprehensive; at the same time, for the consideration of improving the stability of the forecasting model, the weight coefficients are determined according to the historical forecasting performance of the models, and the specific calculation steps are given
Summary
In the forecasting of ship maintenance costs in of in China, due to the large time span of costs and many influencing factors, the collection of relevant data started late[13],using traditional cost estimation methods and individual forecasting models is difficult to describe its changing law[4].Bates and Granger[5] proposed the concept of combination forecasting in 1969. They recognized the difficulty of constructing real models. Based on the above content, the integrated weight coefficients of the combination forecasting is determined, and the comparative analysis is carried out in combination with examples and common combination forecasting methods
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