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)
| Parent link: | Proceedings of SPIE.— .— Bellingham: SPIE Vol. 13217 : Digital Technologies, Optics, and Materials Science (DTIEE 2024).— 2024.— Article number 132170L, 6 p. |
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| Other Authors: | , |
| 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
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| Subjects: | |
| 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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| 200 | 1 | |a Basic minimum stack of experiments in time series forecasting with ARIMA model |f Igor Botygin, Vladislav Sherstnev, Anna Sherstneva | |
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| 330 | |a 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 | ||
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