Impact of Chaining Method and Level of Completion on Accuracy of Function Structure-Based Market Price Prediction Models

[+] Author and Article Information
Amaninder Singh Gill

315 Olin Engineering Complex Apt - 1058 Melbourne, FL 32901 gilla2018@my.fit.edu

Joshua D. Summers

Mechanical Engineering Dept 203 EIB (Fluor Daniel Building) Clemson, SC 29634-0921 jsummer@clemson.edu

Chiradeep Sen

150 W University Blvd Melbourne, FL 32901 csen@fit.edu

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the Journal of Computing and Information Science in Engineering. Manuscript received April 2, 2018; final manuscript received March 12, 2019; published online xx xx, xxxx. Assoc. Editor: Rahul Rai.

ASME doi:10.1115/1.4043210 History: Received April 02, 2018; Accepted March 12, 2019


The goal of this paper is to explore how different modeling approaches for constructing function structure models and different levels of model completion affect the ability to make inferences (reason) on the resulting information within the respective models. Specifically, the function structure models are used to predict market prices of products, predictions that are then compared based on their accuracy and precision. This work is based on previous studies on understanding how function modeling and the use of topological information from design graphs to predict information with historical training. It was found that forward chaining was the least favorable chaining type irrespective of the level of completion whereas the backward chaining models functioned relatively better across all completion levels. Given the poor performance of the nucleation models at the highest level of completion, future research must be directed towards understanding and employing the methods yielding the most accuracy. Moreover, the results from this simulation based study can be used to develop modeling guidelines for designers or students when constructing function models.

Copyright © 2019 by ASME
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