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

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), 2022. Published by Science Publishing Group

Keywords

Rainfall, Temperature, Average, SARIMA, Forecasting, Models

References
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[3] Akaike, H. (1974). “A new look at the statistical model identification”. IEEE Transactions of Automatic Control, 19 (6): 716-723.
[4] Bee Dagum, E. (2010). Time series modeling and decomposition. Statistica, 70 (4), 433–457. https://doi.org/10.6092/issn.1973-2201/3597.
[5] Box, G. E. P., and David A. Pierce. “Distribution of Residual Autocorrelations in Autoregressive-Integrated Moving Average Time Series Models.” Journal of the American Statistical Association 65, no. 332 (1970): 1509–26. https://doi.org/10.2307/2284333.
[6] Carlos M. Jarque, Anil K. Bera, Efficient tests for normality, homoscedasticity and serial independence of regression residuals, Economics Letters, Volume 6, Issue 3, 1980, Pages 255-259, ISSN 0165-1765, https://doi.org/10.1016/0165-1765(80)90024-5.
[7] Dickey, D. A., & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series With a Unit Root. Journal of the American Statistical Association, 74 (366), 427–431. https://doi.org/10.2307/2286348.
[8] E. Mair, G. Leitinger, S. Della Chiesa, G. Niedrist, U. Tappeiner & G. Bertoldi (2016) A simple method to combine snow height and meteorological observations to estimate winter precipitation at sub-daily resolution, Hydrological Sciences Journal, 61: 11, 2050-2060, DOI: 10.1080/02626667.2015.1081203.
[9] Hyndman, R. J., & Athanasopoulos, G. (2018) Forecasting: Principles and Practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2.
[10] Jan de Leeuw. (2011). Information Theory and an Extension of the Maximum Likelihood Principle by Hirotogu Akaike. UCLA: Department of Statistics, UCLA. Retrieved from https://escholarship.org/uc/item/0fd986xb.
[11] National Weather Service, National Operational Hydrological Remote Sensing Center. Retrieved from https://www.nohrsc.noaa.gov/
[12] National Research Council. 2010. When Weather Matters: Science and Services to Meet Critical Societal Needs. Washington, DC: The National Academies Press. https://doi.org/10.17226/12888.
[13] Prema. V, Rao, U. (2015), Time series decomposition model for accurate wind speed forecast, Renewables: Wind, Water, and Solar V-2, DOI 10.1186/s40807-015-0018-9.
[14] Rong-Gang Cong, Mark Brady, “The independence between Rainfall and Temperature: Copula Analysis”, The Scientific Journal, vol. 2012, Article ID 405675, 11 pages, 2012. https://doi.org/10.1100/2012/405675.
[15] Schwarz, G. (1978). “Estimating the dimension of a model”. Annals of Statistics, 6 (2): 461-464.
[16] Sugiura, N. (1978). “Further analysis of the data by Akaike’s information criterion and the finite correlations”. Communications of Statistics-Theory and Methods, A7: 13-26.
[17] Tektaş, Mehmet. (2010). Weather forecasting using ANFIS and ARIMA models. A case study for Istanbul. Environmental Research, Engineering and Management 51 (1): 5–10.
[18] Teshome Hailemeskel Abebe. Time Series Analysis of Monthly Average Temperature and Rainfall Using Seasonal ARIMA Model (in Case of Ambo Area, Ethiopia). International Journal of Theoretical and Applied Mathematics. Vol. 6, No. 5, 2020, pp. 76-87. doi: 10.11648/j.ijtam.20200605.13.
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Cite This Article
  • 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
  • Department of Mathematics and Computer Science, Minot State University, Minot, the United States

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

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

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