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. © 2022 ACM.
ABSTRACT
In this paper, we compare the three countries, India, Japan, and Indonesia's Twitter topics concerning COVID-19. The tweet data were collected from the period of April 2021 to June 2021. The damage of COVID-19 in India and Indonesia was unprecedented in our human beings history. Unexpectedly we could collect Tweets concerning the pandemic. From the data, we would like to extract unexpected topics to prepare for future challenges from humanitarian standpoints. In Japan, the female suicide rate raised significantly. In India, the Joint Entrance Examination were cancelled due to the pandemic, which caused irregular educational systems timeline for Indian students which might create a lot of concerns in future. In Indonesia, Tweets during the peak period on June 2021 have shown some record of discussion on the scarcity of oxygen tubes at isolation houses during the surge of COVID-19 pandemic cases. The results of the analysis showed us the growing fear of the infection, the situation of lack of oxygen tubes, the sad news of the increase in suicides in Japan, the confusion of the entrance examination system nationwide in India, and services related to the distribution of subsidies from the government in Indonesia.
ABSTRACT
With the increasing proliferation of mobile phone, internet and communication technologies, social network sites (SNS) are gaining importance worldwide. People express and exchange their opinion on various social network sites like twitter, facebook, blogs over different local and global issues. Efficient analysis of such vast text data from SNS provides a good way of understanding insights of public opinion, government policy and social condition of different countries. Topic modeling is a popular tool for extracting information from text data. Dynamic topic tracking and its visualization provides a means for capturing the change of topics over time which is important for visualization of changing needs of the society and keeping updated with the current situation. In this work, COVID-19 related twitter data in two different languages are collected and analyzed by dynamic topic model to track the spread of the evolved topics during the pandemic in two different countries in order to visualize the differences and commonness of the effect of pandemic. Here we mainly focused on the tweet data related to Japan and India in Japanese and English respectively. It is found that the country specific characteristics are prominent in some topics while some topics express the general concerns during the pandemic. This study seems to be effective to provide a technique for capturing the opinion and needs of people during a pandemic by analysis of tweet data. © 2021 IEEE.
ABSTRACT
In this paper, clustering for stock data is conducted with two clustering methods, k-Shape and k-means with DTW distance measure and the results are compared. The data is the top 129 global electronics manufactures' stock prices from 2018 to 2020 which included the worst Christmas in 2018 and the beginning of COVID-19 outbreak. The involved countries are US, China, Taiwan, Korea, Japan and some others. The clustering results by k-Shape indicate distinctively different effects on those countries' stock markets due to the COVID-19 turmoil. The patterns of the clusters can be visualized to identify the differences among the clusters. We found that each of eight clusters comprises of the same country companies. From that, we could guess that investors or their algorithms tend to invest in companies according to its country rather than the individual company's performance. © 2021 IEEE.
ABSTRACT
In this paper, we compare global automobile manufacturing companies' stock price movement under the pandemic in 2020. The purpose of this work is to investigate the stock price movement of top automobile manufacturing companies. Here, we used machine learning based time series data clustering method. We considered the period of time series stock data from 2020/01/02 to 2021/03/18. In March 2020, around the world, the worst stock price plunge was caused by COVID-19. Then almost all global automakers' stock prices were severely damaged. They, however, recovered gradually their stock prices. On the stock prices, investors' expectations are reflected. The recovery pattern of stock prices can mean the investors' evaluation of the companies. The result of the clustering, contrary to our expectations, shows that the stock prices were likely to move depending on the country, instead of individual companies' performance. The country-based clusters we found are a Japanese companies' cluster, two USA companies' clusters, and two Chinese companies' clusters. In addition, two regional clusters were found which are Asian region cluster and EU region cluster. In the paper we will describe the differences of stock movement patterns among the country-based clusters. © 2021 IEEE.