A, M. Adnan., J, P. I., & S, R. M. (2023). Forecasting Consumer Price Index (CPI) Using Deep Learning and Hybrid Ensemble Technique. 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), 1–8. https://doi.org/10.1109/ACCAI58221.2023.10200153
Aparicio, D., & Bertolotto, M. I. (2020). Forecasting inflation with online prices. International Journal of Forecasting, 36(2), 232–247. https://doi.org/10.1016/j.ijforecast.2019.04.018
Boniface, A., & Martin, A. (2019). Time Series Modeling and Forecasting of Consumer Price Index in Ghana. Journal of Advances in Mathematics and Computer Science, 1–11. https://doi.org/10.9734/jamcs/2019/v32i130134
Chhorn, T., & Chaiboonsri, C. (2018). Modelling and Forecasting Tourist Arrivals to Cambodia: An Application of ARIMA-GARCH Approach. Journal of Management, Economics and Industrial Organization, 1–19. https://doi.org/10.31039/jomeino.2018.2.2.1
Corpin, S. J. T., Marbella, J. N. P., Kua, S. J. J., Mabborang, R. C., & Lamprea, C. T. (2023). Forecasting Inflation Rate in the Philippines Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Model. European Journal of Computer Science and Information Technology, 11(2), 13–36. https://doi.org/10.37745/ejcsit.2013/vol11n21336
Costales, J. A. (2021). Cost Modeling and Analysis of the Consumer Price Index in the Philippines. 2021 10th International Conference on Software and Computer Applications, 32–38. https://doi.org/10.1145/3457784.3457836
Divisekara, R. W., Jayasinghe, G. J. M. S. R., & Kumari, K. W. S. N. (2021). Forecasting the red lentils commodity market price using SARIMA models. SN Business & Economics, 1(1), 20. https://doi.org/10.1007/s43546-020-00020-x
Dum, Z., & Essi, I. D. (2017). Modeling Price Volatility of Nigerian Crude Oil Markets Using GARCH Model: 1987-2017. 3(4).
Emong Herbert Robert, A. M. M., & Mahmoud A. Abdel-Fattah. (2022). Evaluation of a Functional Time Series Model for Forecasting Inflation in Uganda. Journal of Statistics Applications & Probability, 11(2), 523–534. https://doi.org/10.18576/jsap/110213
Fahrudin, R., & Sumitra, I. D. (2019). Forecasting Inflation Using Seasonal Autoregressive Integrated Moving Average Method for Estimates Decent Living Costs. IOP Conference Series: Materials Science and Engineering, 662(2), 022062. https://doi.org/10.1088/1757-899X/662/2/022062
Gjika Dhamo, E., Puka, L., & Zaçaj, O. (2018, September 6). Forecasting Consumer Price Index (Cpi) Using Time Series Models And Multi Regression Models (Albania Case Study). 10th International Scientific Conference “Business and Management 2018.” Business and Management 2018, Vilnius Gediminas Technical University, Lithuania. https://doi.org/10.3846/bm.2018.51
Hadwan, M., M. Al-Maqaleh, B., N. Al-Badani, F., Ullah Khan, R., & A. Al-Hagery, M. (2022). A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting. Computers, Materials & Continua, 70(3), 4829–4845. https://doi.org/10.32604/cmc.2022.017824
Ibrahim, A., Sani, U. M., & Olokojo, V. O. (2023). Forecasting Consumer Price Index and Exchange Rate Using Arima Models: Empirical Evidence from Nigeria. Fudma Journal of Sciences, 6(6), 114–124. https://doi.org/10.33003/fjs-2022-0606-1136
Konarasinghe, K. M. U. B. (2022). Modeling Consumer Price Index of Thailand. https://doi.org/10.5281/ZENODO.5876872
Koula, J., Tiho, T., & Christophe Chiapo, A. (2020). On the Analysis and Modelling of the Harmonized Consumer Price Indices of West African Economic and Monetary Union Member States. American Journal of Theoretical and Applied Statistics, 9(6), 283. https://doi.org/10.11648/j.ajtas.20200906.14
Lidiema, C. (2017). Modelling and Forecasting Inflation Rate in Kenya Using SARIMA and Holt-Winters Triple Exponential Smoothing. American Journal of Theoretical and Applied Statistics, 6(3), 161. https://doi.org/10.11648/j.ajtas.20170603.15
Magnus Ogolo, I., & Lekia, N. (2022). Univariate Time Series Analysis of Consumer Price Index on Food and Non-alcoholic Beverages. Journal of Mathematical Sciences & Computational Mathematics, 3(4), 416–440. https://doi.org/10.15864/jmscm.3402
Majhi, S. K., Bano, R., Srichan, S. K., Acharya, B., Al-Rasheed, A., Alqahtani, M. S., Abbas, M., & Soufiene, B. O. (2023). Food price index prediction using time series models: A study of Cereals, Millets and Pulses [Preprint]. In Review. https://doi.org/10.21203/rs.3.rs-2999898/v1
Milunovich, G. (2020). Forecasting Australia’s real house price index: A comparison of time series and machine learning methods. Journal of Forecasting, 39(7), 1098–1118. https://doi.org/10.1002/for.2678
Mustapha, M., Seri, M., & Abubakar, Z. M. (2021). Forecasting Nigeria’s Inflation Using Sarima Modeling.
Muthu, N. S., Kannan, K. S., Deneshkumar, V., & Thangasamy, P. (2021). SARIMA Model for Forecasting Price Indices Fluctuations. European Journal of Mathematics and Statistics, 2(6), 1–6. https://doi.org/10.24018/ejmath.2021.2.6.67
Naden, T. P., & Etuk, E. H. (2017). Sarima Modeling of Nigerian Food Consumer Price Indices. 2(4).
Ngailo, E., Luvanda, E., & Massawe, E. S. (2014). Time Series Modelling with Application to Tanzania Inflation Data. Journal of Data Analysis and Information Processing, 02(02), 49–59. https://doi.org/10.4236/jdaip.2014.22007
Nyoni, T. (2019). Modeling and forecasting inflation in Tanzania using ARIMA models. https://mpra.ub.uni-muenchen.de/92458/
Rapoo, M. I., Chanza, M. M., & Motlhwe, G. (2022). Inflation Rate Modelling Through a Hybrid Model of Seasonal Autoregressive Moving Average and Multilayer Perceptron Neural Network: In I. R. Management Association (Ed.), Research Anthology on Macroeconomics and the Achievement of Global Stability (pp. 551–567). IGI Global. https://doi.org/10.4018/978-1-6684-7460-0.ch030
Rohmah, M. F., Putra, I. K. G. D., Hartati, R. S., & Ardiantoro, L. (2021). Comparison Four Kernels of SVR to Predict Consumer Price Index. Journal of Physics: Conference Series, 1737(1), 012018. https://doi.org/10.1088/1742-6596/1737/1/012018
Sibai, F. N., Asaduzzaman, A., El-Moursy, A., & Sibai, A. (2021). Forecasting the Consumer Price Index: A Comparative Study of Machine Learning Methods.
Tang, X., Wang, L., Cheng, J., & Chen, J. (2019). Forecasting model based on information-granulated GA-SVR and ARIMA for producer price index. https://doi.org/10.48550/ARXIV.1903.12012
Uwilingiyimana, C., Munga’Tu, J., & Harerimana, J. de D. (2015). Forecasting Inflation in Kenya Using Arima—Garch Models. 3(2).
Wanjuki, T. M., Wagala, A., & Muriithi, D. K. (2022). Evaluating the Predictive Ability of Seasonal Autoregressive Integrated Moving Average (SARIMA) Models using Food and Beverages Price Index in Kenya. European Journal of Mathematics and Statistics, 3(2), 28–38. https://doi.org/10.24018/ejmath.2022.3.2.100
Wanto, A., Fauzan, M., Suhendro, D., Parlina, I., Damanik, B. E., Siregar, P. A., & Hidayati, N. (2018). Epoch Analysis and Accuracy 3 ANN Algorithm using Consumer Price Index Data in Indonesia: Proceedings of the 3rd International Conference of Computer, Environment, Agriculture, Social Science, Health Science, Engineering and Technology, 35–41. https://doi.org/10.5220/0010037400350041
Xu, X., & Zhang, Y. (2023). Retail Property Price Index Forecasting through Neural Networks. Journal of Real Estate Portfolio Management, 29(1), 1–28. https://doi.org/10.1080/10835547.2022.2110668
Zahara, S., Sugianto, & Ilmiddaviq, M. B. (2020). Consumer price index prediction using Long Short Term Memory (LSTM) based cloud computing. Journal of Physics: Conference Series, 1456(1), 012022. https://doi.org/10.1088/1742-6596/1456/1/012022
Zhang, S.-Y., Lin, Z., & Yhang, W.-J. (2023). Forecasting CPI of restaurants and hotels in Korea using the seasonal autoregressive integrated moving average (SARIMA) model. International Journal of Tourism and Hospitality Research, 37(4), 85–94. https://doi.org/10.21298/IJTHR.2023.4.37.4.85
Zhang, X. (2023). Forecast and Analysis of China’s CPI Based on SARIMA Model. In D. Qiu, Y. Jiao, & W. Yeoh (Eds.), Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022) (Vol. 5, pp. 1354–1361). Atlantis Press International BV. https://doi.org/10.2991/978-94-6463-030-5_135
Zhao, L. L., Wang, B., Mbachu, J., & Egbelakin, T. (2020). Using artificial neural networks to forecast producer price index for New Zealand. International Journal of Internet Manufacturing and Services, 7(3), 191. https://doi.org/10.1504/IJIMS.2020.107944
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