Automated System and Machine Learning Application in Economic Activity Monitoring and Nowcasting

ABSTRACT

The amount of data is growing at an extraordinary rate each year. Nowadays, data is used in various fields. One of these areas is economics, which is significantly linked to data analysis. Policymakers, financial institutions, investors, businesses, and households make economic decisions in real-time. These decisions need to be taken even faster in various economic shocks, such as the financial crisis, COVID-19, or war. For this reason, it is important to have data in as frequent a range as possible, as only such data will reliably assess the economic situation. Therefore, automated systems are required to collect, transform, analyse, visualise, perform other operations, and interpret the results. This paper presents the concept of economic activity, classical and alternative indicators describing the economic activity, and describes the automated economic activity monitoring system. Due to the different economic structures and the different availability of data in different countries, these systems are not universal and can only be adapted to specific countries. The developed automated system uses working intelligence methods to predict the future values of indicators, perform clustering, classification of observations, or other tasks. The application’s developed user interface allows users to use different data sources, analyses, visualisations, or results of machine learning methods without any programming knowledge. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.