Time Series Analysis of SHAP Values by Automobile Manufacturers Recovery Rates

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.