skip to main content
10.1145/3556677.3556697acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicdltConference Proceedingsconference-collections
research-article

Time Series Analysis of SHAP Values by Automobile Manufacturers Recovery Rates

Published: 08 October 2022 Publication History

Abstract

In this paper, we propose a method for evaluating SHAP values by time series change. SHAP values are based on the Shapley theory and have been widely used to interpret the machine-learning based regression results. The SHAP approach plays an important role in the machine-learning regression analysis. We apply the SHAP approach to the time series analysis which is effective when the target values fluctuate but the explanatory variable values have little variation over a long time, such as behavior characteristics of a company. In the paper, the automobile manufacturing industry data just after the outbreak of COVID-19 were used. After this stock prices’ worst plunge, many automakers’ stock prices had been recovered and started again growing rapidly. We conducted the regressions of which target variable were the recovery rates to find the important factors for the recoveries. The regression method we used is XGBoost. As a result, we found that an explanatory variable “sales growth ratio” was the most important factor for the stock recovery. In addition, the individual companies' important factors could be evaluated as time series data in detail, using the SHAP sequences. This SHAP-based time series analysis method is applicable to various fields.

References

[1]
L. S. Shapley, "A value for n-person games, Contributions to the Theory of Games, 2, 307–317," ed: Princeton University Press, Princeton, NJ, USA, 1953.
[2]
A. E. Roth, "Introduction to the Shapley value," The Shapley value, pp. 1-27, 1988.
[3]
A. E. Roth, The Shapley value: essays in honor of Lloyd S. Shapley. Cambridge University Press, 1988.
[4]
E. Winter, "The shapley value," Handbook of game theory with economic applications, vol. 3, pp. 2025-2054, 2002.
[5]
S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," Advances in neural information processing systems, vol. 30, 2017.
[6]
A. Joseph, "Shapley regressions: A framework for statistical inference on machine learning models," presented at the King's Business School Working Paper, 2019.
[7]
D. Lubo-Robles, D. Devegowda, V. Jayaram, H. Bedle, K. J. Marfurt, and M. J. Pranter, "Machine learning model interpretability using SHAP values: Application to a seismic facies classification task," in SEG International Exposition and Annual Meeting, 2020: OnePetro.
[8]
W. Zeng, A. Davoodi, and R. O. Topaloglu, "Explainable DRC hotspot prediction with random forest and SHAP tree explainer," presented at the Proceedings of the 23rd Conference on Design, Automation and Test in Europe, Grenoble, France, 2020.
[9]
D. Slack, S. Hilgard, E. Jia, S. Singh, and H. Lakkaraju, "Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods," in Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society: Association for Computing Machinery, 2020, pp. 180–186.
[10]
K. E. Mokhtari, B. P. Higdon, and A. Başar, "Interpreting financial time series with SHAP values," presented at the Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering, Toronto, Ontario, Canada, 2019.
[11]
K. Yamaguchi, "Intrinsic Meaning of Shapley Values in Regression," in International Conference on Awareness Science and Technology (iCAST), Morioka, Japan, 2020, pp. 1-6: IEEE.
[12]
K. Yamaguchi, "Feature Importance Analysis in Global Manufacturing Industry," International Journal of Trade, Economics Finance, vol. 13, no. 2, pp. 28-35, 2022.
[13]
Y. Shirota, M. Fujimaki, E. Tsujiura, M. Morita, and J. A. D. Machuca, "A SHAP Value-Based Approach to Stock Price Evaluation of Manufacturing Companies," in 2021 4th International Conference on Artificial Intelligence for Industries (AI4I), 2021, pp. 75-78: IEEE.
[14]
T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," presented at the Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, 2016.
[15]
N. Sharma, XGBoost. The Extreme Gradient Boosting for Mining Applications. Norderstedt, Germany: GRIN Verlag, 2017.
[16]
J. Brownlee, XGBoost With Python (Machine Learning Master). 2020.
[17]
N. Sharma, XGBoost. The Extreme Gradient Boosting for Mining Applications. GRIN Verlag, 2018.
[18]
S. M. Lundberg and S.-I. Lee, "A unified approach to interpreting model predictions," in Advances in neural information processing systems, 2017, pp. 4765-4774.
[19]
S. M. Lundberg, G. G. Erion, and S.-I. Lee, "Consistent individualized feature attribution for tree ensembles," arXiv preprint arXiv:1802.03888, 2018.
[20]
A. D. Haimovich, "Development and validation of the quick COVID-19 severity index: a prognostic tool for early clinical decompensation," Annals of emergency medicine, vol. 76, no. 4, pp. 442-453, 2020.
[21]
C. Zhang, L. Shi, and F.-S. Wang, "Liver injury in COVID-19: management and challenges," The lancet Gastroenterology & hepatology, vol. 5, no. 5, pp. 428-430, 2020.
[22]
K. Yamaguchi, "Intrinsic Meaning of Shapley Values in Regression," in 11th International COnf. on Awareness Science and Technology (iCAST), Morioka, Japan, 2020, pp. 1-6: IEEE.
[23]
C. Frye, C. Rowat, and I. Feige, "Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability," Advances in Neural Information Processing Systems, vol. 33, 2020.
[24]
C. Frye, D. de Mijolla, L. Cowton, M. Stanley, and I. Feige, "Shapley-based explainability on the data manifold," arXiv e-prints, p. arXiv: 2006.01272, 2020.
[25]
L. Ibrahim, M. Mesinovic, K.-W. Yang, and M. A. Eid, "Explainable prediction of acute myocardial infarction using machine learning and shapley values," IEEE Access, vol. 8, pp. 210410-210417, 2020.

Cited By

View all
  • (2023)Impact Analysis of Supply Chain Competence on Market Capital Growth in Automobile Manufacturers2023 14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAI-AAI59060.2023.00090(438-441)Online publication date: 8-Jul-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICDLT '22: Proceedings of the 2022 6th International Conference on Deep Learning Technologies
July 2022
155 pages
ISBN:9781450396936
DOI:10.1145/3556677
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 October 2022

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. COVID-19
  2. automaker
  3. stock price
  4. time series analysis of SHAP values

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICDLT 2022

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)57
  • Downloads (Last 6 weeks)7
Reflects downloads up to 26 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2023)Impact Analysis of Supply Chain Competence on Market Capital Growth in Automobile Manufacturers2023 14th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI)10.1109/IIAI-AAI59060.2023.00090(438-441)Online publication date: 8-Jul-2023

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media