Using the Conditional Maximum Likelihood function with the genetic algorithm to estimate STARIMA models (pλ, d, qm)
DOI:
https://doi.org/10.33095/mxsvs771Keywords:
spatiotemporal series; STARIMA; weight matrices; Inverse Distance; Conditional Maximum Likelihood Estimation; Genetic Algorithm.Abstract
Strategic planning for a specific phenomenon depends mainly on accurate prediction by developing a model to represent that phenomenon. Therefore, this research dealt with spatiotemporal series models such as the STARIMA model (pλ, d, qm) model when there is a spatial correlation for neighbouring sites and a spatial correlation for the same geographical location. The methods for estimating the parameters are the Conditional Maximum Likelihood method and the method of estimating the parameters using the genetic algorithm (MLEGA). These models were compared using the statistical comparison metrics (RMSE). The data represents the daily infections with the COVID-19 epidemic for (10) sectors on the Rusafa side of the city of Baghdad from 29/2/2020 until 25/4/2023; it was found that spatiotemporal series data consider the analysis of spatial relationships between geographic points, which allows for a better understanding of the development of phenomena across time and space. The applied results reached the superiority of the spatiotemporal model STARIMA (11,1,31) with the presence of spatial correlation of neighbouring sites using the genetic algorithm because it has a lower (RMSE), so it was used to predict the daily infections of the Covid-19 epidemic for (10) sectors on the Rusafa side of the city of Baghdad For the period from 26/2/2020 until 5/5/2023.
Paper type :Research paper.
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Ahmed, A. and Ismael, B. 2022. Using a hybrid SARIMA-NARNN Model to Forecast the Numbers of Infected with (COVID-19) in Iraq. journal of Economics and Administrative Sciences. 28(132), pp. 113-118.
Alatta, H.J., George, L.E., Behadili, S.F., Sayyid, B.H. and Ali, M.H., 2023. Geospatial Data Analysis of School Distribution in Baghdad City. Iraqi Journal of Science, pp.6675-6685.
Al-Ramahi, F.K.M., Hasan, M.H. and Zaeen, A.A., 2022. Spatial Analysis of Relative Humidity and Its Effect on Baghdad City for The Years 2008, 2013 and 2018. Iraqi Journal of Science, pp.3236-3250.
Al-ramahi, F.K.M., Shnain, A.A. and Ali, A.B., 2022. The Modern Techniques in Spatial Analysis to Isolate, Quarantine the Affected Areas and Prevent the Spread of COVID-19 Epidemic. Iraqi Journal of Science, pp.4102-4117.
Al-ramahi, F.K.M., Shnain, A.A. and Ali, A.B., 2022. The Modern Techniques in Spatial Analysis to Isolate, Quarantine the Affected Areas and Prevent the Spread of COVID-19 Epidemic. Iraqi Journal of Science, pp.4102-4117.
André, M., Dabo-Niang, S., Soubdhan, T. and Ould-Baba, H., 2016. Predictive spatio-temporal model for spatially sparse global solar radiation data. Energy, 111, pp.599-608.
Awwad, F.A., Mohamoud, M.A. and Abonazel, M.R., 2021. Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling. PLoS One, 16(4), p.e0250149.
Berkani, S., Guermah, B., Zakroum, M. and Ghogho, M., 2023. Spatio-Temporal Forecasting: a Survey of Data-Driven Models using Exogenous Data. IEEE Access.
Cheng, T., Wang, J., Haworth, J., Heydecker, B. and Chow, A., 2014. A dynamic spatial weight matrix and localized space–time autoregressive integrated moving average for network modeling. Geographical Analysis, 46(1), pp.75-97.
Cliff, A.D., and Ord, J.K., 1975 Space-Time modelling with an application to regional forecasting. Transactions of the Institute of British Geographers, 64, 119-128.
De Barros, O.M., Marte, C.L., Isler, C.A., Yoshioka, L.R. and da Fonseca Junior, E.S., 2023. Spatial matrices for short-term traffic forecasting based on time series. Latin American transport studies, 1, p.100007.
Faisal, F., Novianti, P. and Rizal, J., 2018. The application of spatial analysis and time series in modeling the frequency of earthquake events in bengkulu province. Aceh International Journal of Science and Technology, 7(2), pp.103-114.
Halim, S., Bisono, I.N., Sunyoto, D. and Gendo, I., 2009, December. Parameter estimation of space-time model using genetic algorithm. In 2009 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 1371-1375). IEEE.
Hathal, H. and Manfi, S. 2023. An Artificial Intelligence Algorithm to Optimize the Classificationof the HepatitisType. journal of Economics and Administrative Sciences. 29(135), pp. 43-55.
Kamarianakis, Y. and Prastacos, P., 2003. Space-Time modeling of traffic flow. The Regional Economics Applications Laboratory.
Kareem, A.M. and Al-Azzawi, S.N., 2021. A stochastic differential equations model for the spread of coronavirus COVID-19): the case of Iraq. Iraqi Journal of Science, pp.1025-1035.
Khalil, Z. and Ahmmed, F. 2021. Predicting Social Security Fund compensation in Iraq using ARMAX Model. journal of Economics and Administrative Sciences. 127(125), pp. 493-508.
Kumar, R.R., Sarkar, K.A., Dhakre, D.S. and Bhattacharya, D., 2023. A Hybrid Space–Time Modelling Approach for Forecasting Monthly Temperature. Environmental Modeling & Assessment, 28(2), pp.317-330.
Munandar, D., Ruchjana, B.N., Abdullah, A.S. and Pardede, H.F., 2023. Literature Review on Integrating Generalized Space-Time Autoregressive Integrated Moving Average (GSTARIMA) and Deep Neural Networks in Machine Learning for Climate Forecasting. Mathematics, 11(13), p.2975.
Pfeifer, P.E. and Deutrch, S.J., 1980. A three-stage iterative procedure for space-time modeling phillip. Technometrics, 22(1), pp.35-47.
Pfeifer, P.E. and Deutsch, S.J., 1980. A STARIMA model-building procedure with application to description and regional forecasting. Transactions of the Institute of British Geographers, pp.330-349.
Pfeifer, P.E., and Deutsch, S.J., 1981a. Variance of the Sample-Time Autocorrelation Function of Contemporaneously Correlated Variables. SIAM Journal of Applied Mathematics, Series A, 40(1), 133-136.
Pfeifer, P.E., and Deutsch, S.J., 1981b. Seasonal Space-Time ARIMA modeling. Geographical Analysis 13 (2), 117-133.
Pfeifer, P.E., and Deutsch, S.J., 1981c. Space-Time ARMA Modeling with contemporaneously correlated innovations. Technometrics 23 (4), 410-409.
Piantari, E., Rabbani, I.M. and Megasari, R., 2023, October. Seasonal space-time based model for infectious disease prediction. In AIP Conference Proceedings (Vol. 2734, No. 1). AIP Publishing.
Rathod, S., Gurung, B., Singh, K.N. and Ray, M., 2018. An improved space-time autoregressive moving average (STARMA) model for modelling and forecasting of spatio-temporal time-series data. Journal of the Indian Society of Agricultural Statistics, 72(3), pp.239-253.
Saad, W. and ALmohana, F. 2022. A Comparison of a Radial Basis Function Neural Network with other Methods for Estimating Missing Values in Univariate Time Series. journal of Economics and Administrative Sciences. 28(134), pp. 134-146.
Salvador Freire and Rathod, S., 2019. Space Time ARMA Models and their application to Radioactivity in Portugal. Theoretical and Applied Climatology, 142, pp.1271-1282.
Subba Rao, T. and Costa Antunes, A.M., 2004. Spatio-temporal modelling of temperature time series: a comparative study. In Time Series Analysis and Applications to Geophysical Systems: Part I (pp. 123-150). Springer New York.
van Zoest, V., Varotsis, G., Menzel, U., Wigren, A., Kennedy, B., Martinell, M. and Fall, T., 2022. Spatio-temporal predictions of COVID-19 test positivity in Uppsala County, Sweden: a comparative approach. Scientific Reports, 12(1), p.15176.
Zhang, Z., Yan, L. and Gu, Y., 2023, July. ST2T: A Spatio-Temporal Transformer for Cellular Traffic Prediction in Digital Twin Systems. In 2023 IEEE 6th International Conference on Electronic Information and Communication Technology (ICEICT) (pp. 1112-1117). IEEE.
Zou, J., Zhu, J., Xie, P., Xuan, P. and Lai, X., 2018, August. A STARMA model for wind power space-time series. In 2018 IEEE Power & Energy Society General Meeting (PESGM) (pp. 1-5). IEEE.
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