Basic minimum stack of experiments in time series forecasting with ARIMA model

Bibliographic Details
Parent link:	Proceedings of SPIE.— .— Bellingham: SPIE
Main Author:	Botygin I. A. Igor Aleksandrovich
Other Authors:	Sherstnev V. S. Vladislav Stanislavovich, Sherstneva A. I. Anna Igorevna
Summary:	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
Language:	English
Published:	2024
Subjects:	time series the ARIMA model statistical modeling language R dynamic processes forecasting электронный ресурс труды учёных ТПУ
Online Access:	https://doi.org/10.1117/12.3035836
Format:	Electronic Book Chapter
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=680011

Internet

https://doi.org/10.1117/12.3035836