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  • Systems of Quantitative Characteristics (Financial, Manufacturing, etc.) Forecasting Based on Spectral and Correlative Time Series Models

Systems of Quantitative Characteristics (Financial, Manufacturing, etc.) Forecasting Based on Spectral and Correlative Time Series Models

Student: Shilov Vladimir

Supervisor: Timofey Shevgunov

Faculty: Graduate School of Business

Educational Programme: Business Informatics (Bachelor)

Final Grade: 8

Year of Graduation: 2019

The problem of analyzing and forecasting time series is a classic problem of data analysis. However, this area is actively developing. The growth of research activity in this area is accompanied by an increase in the demand for the application of the predictive and analytical potential of theoretical developments in the business sphere. At the same time, the complexity of using time series analysis and forecasting tools still remains a significant obstacle to their effective use in analytics tasks for users who are not familiar with the theory of time series or who do not have skills to work in software environments within which the discussed tools are developed. The purpose of this work is to increase the efficiency of processing time series by building an information system that implements the functionality for analyzing and predicting quantitative indicators based on various time series models. The work includes reviewing existing approaches to the analysis and forecasting of time series, reviewing existing approaches to the development of information systems, defining of requirements and structure for the information system being developed, developing a described information system. Development is carried out using Python software (with additional packages), PostgreSQL and JavaScript. As a result, an information system based on a client-server architecture was developed, which allows users to apply time series analysis and forecasting tools to loadable data sets online (without the need to develop software scripts).

Full text (added May 15, 2019)

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