ABSTRACT
Firms selling commercial vehicles often face difficulties due to recessions in the globalized economy. Manufacturers are keen to anticipate demand in future quarters to optimize their production schedules. In this study, commercial vehicle production data from a leading Indian automotive manufacturer were analyzed using moving averages, exponential smoothing, seasonal decomposition and autoregressive integrated moving average (ARIMA) models with the goal of forecasting. The results reveal that the ARIMA (0,1,1) model effectively predicts the sectoral downturn coinciding with the global financial crisis of 2008. As life returns to normal after the financial crisis caused by COVID-19, such models may be used to strategically move past the disruption.
ABSTRACT
During the Covid-19 crisis, many networks have sprung up disseminating information. This study examines the influence of religion during the Covid-19 pandemic. It understands religion as a factor capable of mitigating frustrations and critical situations in society. To this end, a data mining analysis was developed for a set of 107,786 tweets collected from the social platform Twitter in the framework of user-generated content (UGC), linked to the Covid-19 related tweets published by @Pontifex and @Pontifex_es. To achieve this goal, hidden insight data extraction and sentiment analysis are carried out, along with the application of social network analysis (SNA) techniques. The main outcome of the study is the positive correlation between the repercussion of the Pope's tweets and the evolution of the Covid-19 incidence in Europe. Finally, the latent dirichlet allocation (LDA) algorithm identifies the relevant topics in the analysis. Copyright © 2022, IGI Global.