MATHEMATICAL PROCESSING OF TIME SERIES CONSTRUCTED FROM SEISMIC DATA

Authors

  • А.М. Osmonkanov Kyrgyz State University of Construction, Transport and Architecture named after N. Isanov image/svg+xml Author
  • E. Turgunaliev Kyrgyz State University of Construction, Transport and Architecture named after N. Isanov image/svg+xml Author
  • А.А. Uraliev Kyrgyz State University of Construction, Transport and Architecture named after N. Isanov image/svg+xml Author
  • М.К. Asanova Kyrgyz State University of Construction, Transport and Architecture named after N. Isanov image/svg+xml Author
  • Т. Erkinbaev Kyrgyz State University of Construction, Transport and Architecture named after N. Isanov image/svg+xml Author

Keywords:

Time series, seismic data, ARIMA model, magnitude, autocorrelation function, stationarity, forecasting, STATISTICA

Abstract

This paper examines the parameters of the ARIMA (autoregressive integrated moving average) model based on seismic time series. The authors tested the series for stationarity and determined the order of the difference (d = 3) and the order of the moving average (q = 3) using sample autocorrelation functions. Using the STATISTICA software package, an algorithm for predicting the behavior of the time series over a five-year period was implemented. The article presents the theoretical foundations for constructing predictors and estimating model errors within the framework of mathematical statistics.

References

1. Бокс Дж., Дженкинс Т. Анализ временных рядов. Прогноз и управление. Вып.1,2.-М.: Мир , 1974.

2. Боровиков В.П., Ивченко Г.И. Прогнозирование в системе STATISTICA в среде WINDOWS -М.: «Финансы и статистика» 2000.

Downloads

Published

2026-06-01

Issue

Vol. 21 No. 1 (2015): СОВРЕМЕННЫЕ ПРОБЛЕМЫ МЕХАНИКИ СПЛОШНЫХ СРЕД

Section

Статьи

Categories