TIME SERIES MODELING AND FORECASTING OF CONSUMER PRICE INDICES: COMPREHENSIVE ARIMA ANALYSIS AND EXPLORATION OF FUTURE TRENDS IN IRAQI MARKET

Authors

  • Mustafa F.Faris Accounting Department, Faculty of Law, Political Science and Management, Soran University, Kurdistan Region-Iraq.

DOI:

https://doi.org/10.26436/hjuoz.2024.12.4.1397

Keywords:

Customer price indexes (CPI), Forecast, ARIMA and Iraqi Market

Abstract

This study aimed to look at information on customer price indexes (CPI) in Iraq, from 2000 to 2023, using the auto-regressive coordinates moving average (ARIMA) demonstrated for prescient investigation. The primary focus of this study was the necessity of a dependable method for determining consumer prices in a constantly shifting landscape. The approach involved obtaining CPI data from the Food and Agriculture Organization of the United Nations, applying the ARIMA (0,2,3) (0,0,1) model with R programming, and conducting a comprehensive analysis that incorporated descriptive statistics, parameter estimation, and validation tests. The key finding revealed that the ARIMA model effectively captured and predicted CPI patterns, consistent with previous theories, demonstrating its efficacy in Iraqi market. In essence, the results of this study provide valuable insights for those involved in financial decision-making, enhancing our understanding of potential buyer price trends and highlighting the significance of utilizing the ARIMA model in CPI analysis in Iraqi market.

References

Al-Musaylh, M. S., Deo, R. C., Adamowski, J. F., & Li, Y. (2018). Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia. Advanced Engineering Informatics, 35, 1-16.

Aldarraji, M., Vega-Márquez, B., Pontes, B., Mahmood, B., & Riquelme, J. C. (2024). Addressing energy challenges in Iraq: Forecasting power supply and demand using artificial intelligence models. Heliyon 10(4).

Andriyani, M. F., Hoyyi, A., & Yasin, H. (2018). Pemodelan Indeks Harga Konsumen di Jawa Tengah dengan Metode Generalized Space Time Autoregressive Seemingly Unrelated Regression (GSTAR-SUR). Jurnal Gaussian, 7(4), 337-347.

Apostolova, E., & Kreek, R. A. (2018). Training and prediction data discrepancies: Challenges of text classification with noisy, historical data. arXiv preprint arXiv:1809.04019.

Bailey, K. (2017). A combined wavelet and ARIMA approach to predicting financial time series Dublin City University].

Bandara, W., Gable, G., & Rosemann, M. (2006). Business process modelling success: An empirically tested measurement model. Proceedings of International Conference on Information Systems,

Beam, A. S., Moore, K. G., Gillis, S. N., Ford, K. F., Gray, T., Steinwinder, A. H., & Graham, A. (2017). GBCAs and risk for nephrogenic systemic fibrosis: a literature review. Radiologic technology, 88(6), 583-589.

Borkin, D., Nemeth, M., & Nemethova, A. (2019). Using Autoregressive Integrated Moving Average (ARIMA) for Prediction of Time Series Data. Intelligent Systems Applications in Software Engineering: Proceedings of 3rd Computational Methods in Systems and Software 2019, Vol. 1 3,

Büyükşahin, Ü. Ç., & Ertekin, Ş. (2019). Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition. Neurocomputing, 361, 151-163.

Clamor, A., Schlier, B., Koether, U., Hartmann, M. M., Moritz, S., & Lincoln, T. M. (2015). Bridging psychophysiological and phenomenological characteristics of psychosis—preliminary evidence for the relevance of emotion regulation. Schizophrenia research, 169(1-3), 346-350.

Datta, S., Granger, C. W., Barari, M., & Gibbs, T. (2007). Management of supply chain: an alternative modelling technique for forecasting. Journal of the Operational Research Society, 58(11), 1459-1469.

Fischer, M., Imgrund, F., Kolb, J., Janiesch, C., Rosenkranz, C., & Winkelmann, A. (2019). The road to success: recommendations for the design of successful business process modeling initiatives.

Ghysels, E., & Marcellino, M. (2018). Applied economic forecasting using time series methods. Oxford University Press.

Gjika, E., Basha, L., Allka, X., & Ferrja, A. (2020). Predicting the Albanian economic development using multivariate Markov chain model. 11th International Scientific Conference „Business and Management 2020 “,

Herrera-Herrera, A. V., Leierer, L., Jambrina-Enríquez, M., Connolly, R., & Mallol, C. (2020). Evaluating different methods for calculating the Carbon Preference Index (CPI): Implications for palaeoecological and archaeological research. Organic Geochemistry, 146, 104056.

Kharimah, F., Usman, M., Widiarti, W., & Elfaki, F. A. (2015). Time series modeling and forecasting of the consumer price index Bandar Lampung. Science International, 27(5 (B)), 4619-4624.

Kurniasari, D., Mukhlisin, Z., Wamiliana, W., & Warsono, W. (2023). PERFORMANCE OF THE ACCURACY OF FORECASTING THE CONSUMER PRICE INDEX USING THE GARCH AND ANN METHODS. BAREKENG: Jurnal Ilmu Matematika dan Terapan, 17(2), 0931-0944.

Li, Z., Han, J., & Song, Y. (2020). On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning. Journal of Forecasting, 39(7), 1081-1097.

Mei, J., & Guo, M. (2022). Comparative Analysis of CPI Index Intelligent Prediction Based on ARIMA & LSTM Model. 2022 2nd Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS),

Mohamed, J. (2020). Time series modeling and forecasting of Somaliland consumer price index: a comparison of ARIMA and regression with ARIMA errors. American Journal of Theoretical and Applied Statistics, 9(4), 143-153.

Noureen, S., Atique, S., Roy, V., & Bayne, S. (2019). Analysis and application of seasonal ARIMA model in Energy Demand Forecasting: A case study of small scale agricultural load. 2019 IEEE 62nd International Midwest Symposium on Circuits and Systems (MWSCAS),

Nyoni, T. (2019). Forecasting Australian CPI using ARIMA models.

Paloviita, M., & Virén, M. (2014). Analysis of forecast errors in micro-level survey data. Bank of Finland Research Discussion Paper(8).

Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Taieb, S. B., Bergmeir, C., Bessa, R. J., Bijak, J., & Boylan, J. E. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38(3), 705-871.

Purwa, T., Nafngiyana, U., & Suhartono, S. (2017). Comparison of arima, transfer function and var models for forecasting cpi, stock prices, and indonesian exchange rate: Accuracy vs. explainability. Education, 2019.

Riofrío, J., Chang, O., Revelo-Fuelagán, E., & Peluffo-Ordóñez, D. H. (2020). Forecasting the Consumer Price Index (CPI) of Ecuador: A comparative study of predictive models. International Journal on Advanced Science, Engineering and Information Technology, 10(3), 1078-1084.

Rippy, D. (2014). The first hundred years of the Consumer Price Index: a methodological and political history. Monthly Lab. Rev., 137, 1.

Shafiee, S., & Topal, E. (2010). An overview of global gold market and gold price forecasting. Resources policy, 35(3), 178-189.

Shapovalenko, N. (2021). A Suite of Models for CPI Forecasting. Visnyk of the National Bank of Ukraine(252), 4-36.

Siami-Namini, S., Tavakoli, N., & Namin, A. S. (2018). A comparison of ARIMA and LSTM in forecasting time series. 2018 17th IEEE international conference on machine learning and applications (ICMLA),

Sulistiyani, E., & Tyas, S. Y. (2019). Success measurement framework for information technology project: A conceptual model. 2019 International Conference on Computer Science, Information Technology, and Electrical Engineering (ICOMITEE),

Udoh, N. S., & Isaiah, A. S. (2018). A predictive model for inflation in Nigeria. CBN Journal of Applied Statistics, 9(2), 103-129.

von Auer, L., & Shumskikh, A. (2022). Retrospective Computations of Price Index Numbers: Theory and Application. Review of Income and Wealth.

Wu, A.-M., Bisignano, C., James, S. L., Abady, G. G., Abedi, A., Abu-Gharbieh, E., Alhassan, R. K., Alipour, V., Arabloo, J., & Asaad, M. (2021). Global, regional, and national burden of bone fractures in 204 countries and territories, 1990–2019: a systematic analysis from the Global Burden of Disease Study 2019. The Lancet Healthy Longevity, 2(9), e580-e592.

Xiong, T., Hu, Z., & Bao, Y. (2012). An improved EEMD-based framework for CPI forecasting. 2012 Fifth International Joint Conference on Computational Sciences and Optimization,

Youness, J., & Driss, M. (2022). An ARIMA model for modeling and forecasting the dynamic of univariate time series: the case of moroccan inflation rate. 2022 International Conference on Intelligent Systems and Computer Vision (ISCV),

Yusupova, A., Pavlidis, N. G., & Pavlidis, E. G. (2019). Adaptive dynamic model averaging with an application to house price forecasting. arXiv preprint arXiv:1912.04661.

Zhang, D., & Mu, H. (2022). Analysis and Research on the Influencing Factors of Regional CPI Based on CVM-AHP Coupling Perspective. Highlights in Business, Economics and Management, 2, 51-59.

Downloads

Published

2024-11-24

How to Cite

Faris, M. (2024). TIME SERIES MODELING AND FORECASTING OF CONSUMER PRICE INDICES: COMPREHENSIVE ARIMA ANALYSIS AND EXPLORATION OF FUTURE TRENDS IN IRAQI MARKET. Humanities Journal of University of Zakho, 12(4), 852–859. https://doi.org/10.26436/hjuoz.2024.12.4.1397

Issue

Section

Humanities Journal of University of Zakho