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

Igor Botygin; Vladislav Sherstnev; Anna Sherstneva

doi:10.1117/12.3035836

31 July 2024 Basic minimum stack of experiments in time series forecasting with ARIMA model

Igor Botygin, Vladislav Sherstnev, Anna Sherstneva

Author Affiliations +

Proceedings Volume 13217, Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024); 132170L (2024) https://doi.org/10.1117/12.3035836
Event: Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024), 2024, Fergana and Bukhara, Uzbekistan

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Igor Botygin, Vladislav Sherstnev, and Anna Sherstneva "Basic minimum stack of experiments in time series forecasting with ARIMA model", Proc. SPIE 13217, Third International Conference on Digital Technologies, Optics, and Materials Science (DTIEE 2024), 132170L (31 July 2024); https://doi.org/10.1117/12.3035836

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE