Predictive Analytics in Automobile Industry: A Comparison between Artificial Intelligence and Econometrics Ford Motor Company

Format:

Conference/Event

Author/Creator:

Shalini, Shalini, author.

Contributor:

Krishnamurthy, Sridharan

Srinivasan, Narasimhan

Conference Name:

WCX 17: SAE World Congress Experience (2017-04-04 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2017

Summary:

AbstractThis study compares the model efficacy of Neural Network and Vector Auto Regression. Further it also analyses the impact of predictors controlling for total industry volume. Understanding both the methodologies has their distinctive advantages and disadvantages. Our empirical findings indicate that based on the characteristics of data such as non-stationary, non-linearity and non-normality paves the way for use of machine learning algorithm relative to econometrics technique. Our results suggest that data type and its characteristics are more important in determining the methodology than the methodology itself. In industry, econometrics methodologies are widely used due to their usage simplicity and its ability to explain the relationships in simple terms

Notes:

Vendor supplied data

Publisher Number:

2017-01-0238

Access Restriction:

Restricted for use by site license