Skip to main content Skip to main navigation menu Skip to site footer
  • Register
  • Login
  • Menu
  • Home
  • Current
  • Archives
  • Announcements
  • About
    • About the Journal
    • Submissions
    • Editorial Team
    • Privacy Statement
    • Contact
  • Register
  • Login

Ecological Questions

Viable forecasting monthly weather data using time series methods
  • Home
  • /
  • Viable forecasting monthly weather data using time series methods
  1. Home /
  2. Archives /
  3. Vol. 34 No. 1 (2023) /
  4. Articles

Viable forecasting monthly weather data using time series methods

Authors

  • Sergij Vambol Department of Occupational and Environmental Safety, National Technical University Kharkiv Polytechnic Institute, Kharkiv, Ukraine https://orcid.org/0000-0002-8376-9020
  • Ramzan Soomro Quaid-e-Millat Government Degree College Liaqatabad Karachi, Pakistan
  • Saghir Pervaiz Ghauri Faculty of Business Administration, Commerce & Economics, Jinnah University for Women, Karachi, Pakistan
  • Azhar Ali Marri Department of Statistics, University of Baluchistan, Quetta, Pakistan
  • Hoang Thi Dung Vietnam National University of Forestry, Xuan Mai, Ha Noi, Vietnam
  • Nazish Manzoor Kohat University of Science and Technology, Pakistan
  • Shella Bano Department of Geology, University of Karachi, Pakistan https://orcid.org/0000-0002-4494-0737
  • Sana Shahid Media and Communication Studies Department, Sindh Madressatul Islam University, Karachi , Pakistan
  • Asadullah Department of Environmental Sciences, Sindh Madressatul Islam University, Karachi, Pakistan
  • Ahmed Farooq Department of Environmental Sciences, Sindh Madressatul Islam University, Karachi , Pakistan
  • Yurii Lutsenko Institute of Public Administration and Research in Civil Protection

DOI:

https://doi.org/10.12775/EQ.2023.003

Keywords

Time series, ADF-test, Decomposition, AR-model, Dummies, ARMA /ARIMA, ACF and PACF

Abstract

The main object of the research was to assess the forecast values of the weather parameters by using three-time series methods such as Decomposition of time series, Autoregressive (AR) model with seasonal dummies and Autoregressive moving average (ARMA) /Autoregressive Integrated moving average (ARIMA) model. A recent phenomenon in weather changing has disturbed the world in general and Pakistan in particular. In Pakistan due to climate change, flood and heat stroke have taken many lives. Stationarity was measured through the Augmented Dickey-Fuller test; results showed that some variables are I(0) and some are I(1). The reliability of the forecast results was examined through the goodness of fit test. For finding the best fit model, the performance measures of various models: Root Mean Squire Error, Mean Absolute Error and Mean Absolute Percentage Error were considered. The model in which the above statistics are the minimum was chosen as the appropriate model. After model analysis and validation, it was observed that AR-model with seasonal dummies was found to be the best fit model between the three models. Meanwhile, the forecasting for the period Jan.2018 to Dec.2018 was made based on the best fit model. Given the future forecasting results, the temperature will be normal at selected stations. The wind and rainfall will also be present. Overall, it was suggested that the obtained findings of meteorological stations' weather might be normal for the coming few months over there, and no chance of heatstroke and flood might be expected. Future studies must be carried out to provide the awareness to well-being regarding ecological hazardous to minimize their economic loss through mass media.

References

Afrifa-Yamoah E., Saeed B.I. & Karim A., 2016, Sarima modelling and forecasting of monthly rainfall in the Brong Ahafo Region of Ghana. World Environment 6(1): 1–9.

Aggarwal P.K., 2003, Impact of climate change on Indian agriculture. Journal of Plant Biology-new Delhi 30(2): 189–198.

Azad A.S., Sokkalingam R., Daud H., Adhikary S.K., Khurshid H., Mazlan S.N.A. & Rabbani, M.B.A., 2022. Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study. Sustainability, 14(3): 1843.

Bari S.H., Rahman M.T., Hussain M.M. & Ray S., 2015, Forecasting monthly precipitation in Sylhet city using ARIMA model. Civil and Environmental Research 7 (1): 69–77.

Gerretsadikan A. & Sharma M.K., 2011, "Modeling and forecasting of rainfall data of Mekele for Tigray region (Ethiopia)", Statistics and Application 9 (1&2): 31–53.

Gholami V. & Sahour H., 2022, Simulation of rainfall-runoff process using an artificial neural network (ANN) and field plots data. Theoretical and Applied Climatology 147(1): 87–98.

Khan R.A., El Morabet R., Mallick J., Azam M., Vambol V., Vambol S., & Sydorenko V., 2021, Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia. Ecological Questions 32(4): 127–133.

Mahsin M.D., Akhter Y. & Begum M., 2012. Modeling rainfall in Dhaka division of Bangladesh using time series analysis. Journal of mathematical modelling and application 1(5): 67–73.

Mir B.I., 2019, An Overview of Corporate Social Responsibility in the Hospitality and Tourism Sector of Pakistan. UW Journal of Management Sciences 3(2): 1–14.

Momani P.E.N.M. & Naill P.E., 2009, Time series analysis model for rainfall data in Jordan: Case study for using time series analysis. American Journal of Environmental Sciences 5 (5): 599.

Nadeem M.U., Waheed Z. & Ghaffar A.M., 2022, Application of HEC–HMS for flood forecasting in Hazara catchment Pakistan, south Asia. International Journal of Hydrology 6(1): 7–12.

Prema V. & Rao K.U., 2015, Time series decomposition model for accurate wind speed forecast. Renewables: Wind, Water, and Solar 2 (1): 1–11.

Shamsnia S.A., Shahidi N., Liaghat A., Sarraf A. & Vahdat S.F., 2011, Modeling of weather parameters using stochastic methods (ARIMA model)(case study: Abadeh Region, Iran). In International conference on environment and industrial innovation 12(1): 282–285.

Sihag P., Sadikhani M. R., Vambol V., Vambol S., Prabhakar A.K. & Sharma N., 2021, Comparative study for deriving stagedischarge–sediment concentration relationships using soft computing techniques. Journal of Achievements in Materials and Manufacturing

Engineering 104(2): 57–76.

Streimikiene D., Rizwan Raheem A., Vveinhardt J., Pervaiz Ghauri S. & Zahid S., 2018, Forecasting tax revenues using time series techniques–a case of Pakistan. Economic research-Ekonomska istraživanja 31(1): 722–754.

Sultana K., Rahim A., Moin N., Aman S. & Ghauri S.P., 2014, Forecasting inflation and economic growth of Pakistan by using two time series methods. International Journal of Business and Economics Research 2 (6): 174–178.

Sultana N. & Hasan M.M., 2015, Forecasting Temperature in the Coastal Area of Bay of Bengal-An Application of Box-Jenkins Seasonal ARIMA Model. Statistics 7(8): 149–159.

Xu W., Peng H., Zeng X., Zhou F., Tian X. & Peng X., 2022, A Hybrid Modeling Method Based on Linear AR and Nonlinear DBN-AR Model for Time Series Forecasting. Neural Processing Letters 54(1): 1–20.

Downloads

  • pdf

Published

2022-10-04

How to Cite

1.
VAMBOL, Sergij, SOOMRO, Ramzan, GHAURI, Saghir Pervaiz, MARRI, Azhar Ali, DUNG, Hoang Thi, MANZOOR, Nazish, BANO, Shella, SHAHID, Sana, ASADULLAH, FAROOQ, Ahmed and LUTSENKO, Yurii. Viable forecasting monthly weather data using time series methods. Ecological Questions. Online. 4 October 2022. Vol. 34, no. 1, pp. 117-126. [Accessed 5 July 2025]. DOI 10.12775/EQ.2023.003.
  • ISO 690
  • ACM
  • ACS
  • APA
  • ABNT
  • Chicago
  • Harvard
  • IEEE
  • MLA
  • Turabian
  • Vancouver
Download Citation
  • Endnote/Zotero/Mendeley (RIS)
  • BibTeX

Issue

Vol. 34 No. 1 (2023)

Section

Articles

License

Copyright (c) 2022 Sergij Vambol, Ramzan Soomro, Saghir Pervaiz Ghauri, Azhar Ali Marri, Hoang Thi Dung, Nazish Manzoor, Shella Bano, Sana Shahid, Asadullah, Ahmed Farooq, Yurii Lutsenko

Creative Commons License

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.

Stats

Number of views and downloads: 841
Number of citations: 0

Search

Search

Browse

  • Browse Author Index
  • Issue archive

User

User

Current Issue

  • Atom logo
  • RSS2 logo
  • RSS1 logo

Information

  • For Readers
  • For Authors
  • For Librarians

Newsletter

Subscribe Unsubscribe

Tags

Search using one of provided tags:

Time series, ADF-test, Decomposition, AR-model, Dummies, ARMA /ARIMA, ACF and PACF
Up

Akademicka Platforma Czasopism

Najlepsze czasopisma naukowe i akademickie w jednym miejscu

apcz.umk.pl

Partners

  • Akademia Ignatianum w Krakowie
  • Akademickie Towarzystwo Andragogiczne
  • Fundacja Copernicus na rzecz Rozwoju Badań Naukowych
  • Instytut Historii im. Tadeusza Manteuffla Polskiej Akademii Nauk
  • Instytut Kultur Śródziemnomorskich i Orientalnych PAN
  • Instytut Tomistyczny
  • Karmelitański Instytut Duchowości w Krakowie
  • Ministerstwo Kultury i Dziedzictwa Narodowego
  • Państwowa Akademia Nauk Stosowanych w Krośnie
  • Państwowa Akademia Nauk Stosowanych we Włocławku
  • Państwowa Wyższa Szkoła Zawodowa im. Stanisława Pigonia w Krośnie
  • Polska Fundacja Przemysłu Kosmicznego
  • Polskie Towarzystwo Ekonomiczne
  • Polskie Towarzystwo Ludoznawcze
  • Towarzystwo Miłośników Torunia
  • Towarzystwo Naukowe w Toruniu
  • Uniwersytet im. Adama Mickiewicza w Poznaniu
  • Uniwersytet Komisji Edukacji Narodowej w Krakowie
  • Uniwersytet Mikołaja Kopernika
  • Uniwersytet w Białymstoku
  • Uniwersytet Warszawski
  • Wojewódzka Biblioteka Publiczna - Książnica Kopernikańska
  • Wyższe Seminarium Duchowne w Pelplinie / Wydawnictwo Diecezjalne „Bernardinum" w Pelplinie

© 2021- Nicolaus Copernicus University Accessibility statement Shop