Basic minimum stack of experiments in time series forecasting with ARIMA model; Proceedings of SPIE; Vol. 13217 : Digital Technologies, Optics, and Materials Science (DTIEE 2024)

Basic minimum stack of experiments in time series forecasting with ARIMA model; Proceedings of SPIE; Vol. 13217 : Digital Technologies, Optics, and Materials Science (DTIEE 2024)

מידע ביבליוגרפי
Parent link:Proceedings of SPIE.— .— Bellingham: SPIE
Vol. 13217 : Digital Technologies, Optics, and Materials Science (DTIEE 2024).— 2024.— Article number 132170L, 6 p.
מחבר ראשי: Botygin I. A. Igor Aleksandrovich
מחברים אחרים: Sherstnev V. S. Vladislav Stanislavovich, Sherstneva A. I. Anna Igorevna
סיכום:Title screen
A scheme and a basic set of software experiments for time series forecasting using an integrated autoregressive-moving average model (Box-Jenkins model) are presented. The model is based on the assumption that there is some relationship between neighboring values of a time series. In particular, the hypothesis is accepted that the time series contains three components: autoregressive, integrated and moving average. The application of the ARIMA model for forecasting time series using the statistical modelling language R - from the stage of data loading and preprocessing to the prediction of future values - is presented
Текстовый файл
AM_Agreement
שפה:אנגלית
יצא לאור: 2024
נושאים:
time series
the ARIMA model
statistical modeling language R
dynamic processes
forecasting
электронный ресурс
труды учёных ТПУ
גישה מקוונת:https://doi.org/10.1117/12.3035836
פורמט: אלקטרוני Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680011

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