Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA)

Upul Rupassara; Dion Udokop; Favour Ozordi

doi:doi:10.11648/j.ijdsa.20220803.12

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Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA)

Upul Rupassara, Dion Udokop, Favour Ozordi

Published in International Journal of Data Science and Analysis (Volume 8, Issue 3)

Received: 12 May 2022 Accepted: 25 May 2022 Published: 31 May 2022

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Abstract

This project analyzes the monthly average rainfall and temperature from 2005 January to 2021 December in Minot, ND, USA. Since both rainfall and temperature time series represent seasonal components, Seasonal Auto Regressive Integrated Moving Average (SARIMA) models were used to forecast the average rainfall and temperature. The main objective was to identify the SARIMA models based on Akaike’s Information Criteria (AIC). The graphical and diagnostic analysis techniques validated the models having the smallest AIC values. Among the competitive tentative models, the SARIMA (2, 0, 0) (2, 0, 1, 12) and SARIMA (1, 0, 1) (2, 0, 1, 12) were found to be the best time series forecasting models that capture the existing pattern of the rainfall and temperature data, respectively. Nevertheless, these models satisfy the model diagnostics test assumptions on the residuals such as randomness, independency, normality, and heteroscedasticity. Therefore, SARIMA (2, 0, 0) (2, 0, 1, 12) and SARIMA (1, 0, 1) (2, 0, 1, 12) models were used to forecast the mean rainfall and temperature, respectively, from the 2022 January to 2023 December.

Published in	International Journal of Data Science and Analysis (Volume 8, Issue 3)
DOI	10.11648/j.ijdsa.20220803.12
Page(s)	82-93
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Rainfall, Temperature, Average, SARIMA, Forecasting, Models

References

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APA Style

Upul Rupassara, Dion Udokop, Favour Ozordi. (2022). Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA). International Journal of Data Science and Analysis, 8(3), 82-93. https://doi.org/10.11648/j.ijdsa.20220803.12

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ACS Style

Upul Rupassara; Dion Udokop; Favour Ozordi. Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA). Int. J. Data Sci. Anal. 2022, 8(3), 82-93. doi: 10.11648/j.ijdsa.20220803.12

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AMA Style

Upul Rupassara, Dion Udokop, Favour Ozordi. Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA). Int J Data Sci Anal. 2022;8(3):82-93. doi: 10.11648/j.ijdsa.20220803.12

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@article{10.11648/j.ijdsa.20220803.12,
  author = {Upul Rupassara and Dion Udokop and Favour Ozordi},
  title = {Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA)},
  journal = {International Journal of Data Science and Analysis},
  volume = {8},
  number = {3},
  pages = {82-93},
  doi = {10.11648/j.ijdsa.20220803.12},
  url = {https://doi.org/10.11648/j.ijdsa.20220803.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220803.12},
  abstract = {This project analyzes the monthly average rainfall and temperature from 2005 January to 2021 December in Minot, ND, USA. Since both rainfall and temperature time series represent seasonal components, Seasonal Auto Regressive Integrated Moving Average (SARIMA) models were used to forecast the average rainfall and temperature. The main objective was to identify the SARIMA models based on Akaike’s Information Criteria (AIC). The graphical and diagnostic analysis techniques validated the models having the smallest AIC values. Among the competitive tentative models, the SARIMA (2, 0, 0) (2, 0, 1, 12) and SARIMA (1, 0, 1) (2, 0, 1, 12) were found to be the best time series forecasting models that capture the existing pattern of the rainfall and temperature data, respectively. Nevertheless, these models satisfy the model diagnostics test assumptions on the residuals such as randomness, independency, normality, and heteroscedasticity. Therefore, SARIMA (2, 0, 0) (2, 0, 1, 12) and SARIMA (1, 0, 1) (2, 0, 1, 12) models were used to forecast the mean rainfall and temperature, respectively, from the 2022 January to 2023 December.},
 year = {2022}
}

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TY  - JOUR
T1  - Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA)
AU  - Upul Rupassara
AU  - Dion Udokop
AU  - Favour Ozordi
Y1  - 2022/05/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ijdsa.20220803.12
DO  - 10.11648/j.ijdsa.20220803.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 82
EP  - 93
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20220803.12
AB  - This project analyzes the monthly average rainfall and temperature from 2005 January to 2021 December in Minot, ND, USA. Since both rainfall and temperature time series represent seasonal components, Seasonal Auto Regressive Integrated Moving Average (SARIMA) models were used to forecast the average rainfall and temperature. The main objective was to identify the SARIMA models based on Akaike’s Information Criteria (AIC). The graphical and diagnostic analysis techniques validated the models having the smallest AIC values. Among the competitive tentative models, the SARIMA (2, 0, 0) (2, 0, 1, 12) and SARIMA (1, 0, 1) (2, 0, 1, 12) were found to be the best time series forecasting models that capture the existing pattern of the rainfall and temperature data, respectively. Nevertheless, these models satisfy the model diagnostics test assumptions on the residuals such as randomness, independency, normality, and heteroscedasticity. Therefore, SARIMA (2, 0, 0) (2, 0, 1, 12) and SARIMA (1, 0, 1) (2, 0, 1, 12) models were used to forecast the mean rainfall and temperature, respectively, from the 2022 January to 2023 December.
VL  - 8
IS  - 3
ER  -

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Author Information

Upul Rupassara

Department of Mathematics and Computer Science, Minot State University, Minot, the United States
Dion Udokop

Department of Mathematics and Computer Science, Minot State University, Minot, the United States
Favour Ozordi

Department of Mathematics and Computer Science, Minot State University, Minot, the United States

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Plain Text BibTeX RIS

APA Style

Upul Rupassara, Dion Udokop, Favour Ozordi. (2022). Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA). International Journal of Data Science and Analysis, 8(3), 82-93. https://doi.org/10.11648/j.ijdsa.20220803.12

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ACS Style

Upul Rupassara; Dion Udokop; Favour Ozordi. Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA). Int. J. Data Sci. Anal. 2022, 8(3), 82-93. doi: 10.11648/j.ijdsa.20220803.12

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AMA Style

Upul Rupassara, Dion Udokop, Favour Ozordi. Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA). Int J Data Sci Anal. 2022;8(3):82-93. doi: 10.11648/j.ijdsa.20220803.12

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@article{10.11648/j.ijdsa.20220803.12,
  author = {Upul Rupassara and Dion Udokop and Favour Ozordi},
  title = {Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA)},
  journal = {International Journal of Data Science and Analysis},
  volume = {8},
  number = {3},
  pages = {82-93},
  doi = {10.11648/j.ijdsa.20220803.12},
  url = {https://doi.org/10.11648/j.ijdsa.20220803.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220803.12},
  abstract = {This project analyzes the monthly average rainfall and temperature from 2005 January to 2021 December in Minot, ND, USA. Since both rainfall and temperature time series represent seasonal components, Seasonal Auto Regressive Integrated Moving Average (SARIMA) models were used to forecast the average rainfall and temperature. The main objective was to identify the SARIMA models based on Akaike’s Information Criteria (AIC). The graphical and diagnostic analysis techniques validated the models having the smallest AIC values. Among the competitive tentative models, the SARIMA (2, 0, 0) (2, 0, 1, 12) and SARIMA (1, 0, 1) (2, 0, 1, 12) were found to be the best time series forecasting models that capture the existing pattern of the rainfall and temperature data, respectively. Nevertheless, these models satisfy the model diagnostics test assumptions on the residuals such as randomness, independency, normality, and heteroscedasticity. Therefore, SARIMA (2, 0, 0) (2, 0, 1, 12) and SARIMA (1, 0, 1) (2, 0, 1, 12) models were used to forecast the mean rainfall and temperature, respectively, from the 2022 January to 2023 December.},
 year = {2022}
}

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TY  - JOUR
T1  - Time Series Analysis in Forecasting Monthly Average Rainfall and Temperature (Case Study, Minot ND, USA)
AU  - Upul Rupassara
AU  - Dion Udokop
AU  - Favour Ozordi
Y1  - 2022/05/31
PY  - 2022
N1  - https://doi.org/10.11648/j.ijdsa.20220803.12
DO  - 10.11648/j.ijdsa.20220803.12
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 82
EP  - 93
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20220803.12
AB  - This project analyzes the monthly average rainfall and temperature from 2005 January to 2021 December in Minot, ND, USA. Since both rainfall and temperature time series represent seasonal components, Seasonal Auto Regressive Integrated Moving Average (SARIMA) models were used to forecast the average rainfall and temperature. The main objective was to identify the SARIMA models based on Akaike’s Information Criteria (AIC). The graphical and diagnostic analysis techniques validated the models having the smallest AIC values. Among the competitive tentative models, the SARIMA (2, 0, 0) (2, 0, 1, 12) and SARIMA (1, 0, 1) (2, 0, 1, 12) were found to be the best time series forecasting models that capture the existing pattern of the rainfall and temperature data, respectively. Nevertheless, these models satisfy the model diagnostics test assumptions on the residuals such as randomness, independency, normality, and heteroscedasticity. Therefore, SARIMA (2, 0, 0) (2, 0, 1, 12) and SARIMA (1, 0, 1) (2, 0, 1, 12) models were used to forecast the mean rainfall and temperature, respectively, from the 2022 January to 2023 December.
VL  - 8
IS  - 3
ER  -

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