Abstract
The modern economy requires an effective analysis of big data, in particular time series of economic indicators. This study explores the possibilities of MongoDB database management system to effectively manage and analyze economic time series related to agriculture, mining, manufacturing, energy, transportation and other industries. The study provides approaches to working with the MongoDB database, which provides effective management and analysis of time series of economic indicators thanks to a flexible data scheme and horizontal scaling capabilities. MongoDB was chosen as the basis due to its ability to efficiently process large amounts of data. On the basis of a hierarchical model and embedded documents, an optimal data structure has been developed for storing indicators, taking into account their change over time. A multi-stage algorithm for calculating the main economic indicators has been developed, which includes data preparation, analysis of dynamics using time series methods, modeling and forecasting based on mathematical models. The data, current trends and mathematical models of the developed algorithms were used to predict and evaluate balances in various fields. The study showed that the information system allows you to effecti Ely isualize and process economic data, determine the relationship between indicators and predict future changes with high accuracy. MongoDB features were used to improve system performance.