FORECASTING TIME SERIES USING EVENT BINDING


UDC 004.855.5
DOI:10.26102/2310-6018/2019.27.4.039

I.N. Kolesnikov


This article discusses the concept of modification of the time series analysis method, focused on integration with clustering methods in real-time training mode. Various methods of forecasting time series and machine learning are analyzed. The method described in the article predicts the behavior of the time series based on large data obtained from various sources and associated with existing transactions in the time series. This approach makes it possible to find the dependence of changes in certain indicators of the considered systems depending on various events. The performed research offers the concept of automated system training in real time with the possibility of further software implementation. The concept under consideration allows you to build forecasts for any time series, depending on various events, news and data that are in the public domain. An approach is proposed that links events to a transaction chart. The advantage of this approach is the ability to find various dependencies between events and various changes in indicators, for example: prices on exchanges, values of social indicators and many others.

Keywords: data analysis, forecasting, time series, big data, cluster analysis, data mining.

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