A modified ARIMA model for forecasting chemical sales in the USA

A modified ARIMA model for forecasting chemical sales in the USA

model is derived, and the methodology is given in detail. The model is constructed depending on some measurement criteria, Akaike and Bayesian information criterion. For the new time series model, a new algorithm has been generated. The forecasting process, one and two steps ahead, is discussed in d...

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Veröffentlicht in:Journal of physics. Conference series 2021-05, Vol.1879 (3), p.32008
Hauptverfasser: Salah, Othman Mahdi, Mahdi, Ghadeer Jasim Mohammed, Al-Latif, Iman Ahmed Abud
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Sprache:eng
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Algorithms
Autoregressive models
Criteria
Data analysis
Datasets
Forecasting
Physics
Sales
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container_title Journal of physics. Conference series
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creator Salah, Othman Mahdi
Mahdi, Ghadeer Jasim Mohammed
Al-Latif, Iman Ahmed Abud
description model is derived, and the methodology is given in detail. The model is constructed depending on some measurement criteria, Akaike and Bayesian information criterion. For the new time series model, a new algorithm has been generated. The forecasting process, one and two steps ahead, is discussed in detail. Some exploratory data analysis is given in the beginning. The best model is selected based on some criteria; it is compared with some naïve models. The modified model is applied to a monthly chemical sales dataset (January 1992 to Dec 2019), where the dataset in this work has been downloaded from the United States of America census (www.census.gov). Ultimately, the forecasted sales for the next three years for chemical sales in the USA is provided.
doi_str_mv 10.1088/1742-6596/1879/3/032008
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subjects Algorithms
Autoregressive models
Criteria
Data analysis
Datasets
Forecasting
Physics
Sales
title A modified ARIMA model for forecasting chemical sales in the USA
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