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

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

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