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Support Vector Machine Based Forecasting Model Selection

Author: WangJunZhong
Tutor: XiaHongShan
School: Nanjing University of Aeronautics and Astronautics
Course: Computer Software and Theory
Keywords: Prediction model Support Vector Machine Flight plan Leg volume Model selection
CLC: F224
Type: Master's thesis
Year: 2006
Downloads: 189
Quote: 1
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Abstract


With the increasingly fierce competition in the civil aviation market , how to develop reasonable and effective flight plan has become an important content of the airlines on the future development of decision-making . The leg volume forecasting is an important step to develop flight plans , the prediction result is an important basis for development of flight plans . Leg volume subject to a variety of factors such as regional economy, geography, tourism and seasonal , single forecasting model can not be applied to all flight segments . Therefore , you need to select the most appropriate forecasting model specific segment . Systematic analysis of the characteristics of the leg volume and existing model selection method , discussed in detail the theory and support vector machine algorithm , combined with support vector machines for the leg volume model selection conducted in-depth research . Firstly, this paper analyzes the characteristics of leg volume , discussed the existing model selection method set forth on this basis the leg volume model selection based on support vector machine . Then discuss in detail the support vector machine principle of kernel functions and parameter selection method , support vector machine training and classification algorithm . In conclusion, the article details the design and implementation of leg volume based on support vector machine model selection . Three prediction model flight plan and its performance optimized management system experimental results show that this method with good results in leg volume anticipation choice .

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CLC: > Economic > Economic planning and management > Economic calculation, economic and mathematical methods > Economic and mathematical methods
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