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Python approach on fuzzy time series Arima (1, 1, 1) model to analyze original and predict results for online retail of fuel booking services | ||
| Journal of Hyperstructures | ||
| دوره 13، شماره 1، 2024، صفحه 142-155 اصل مقاله (972.12 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22098/jhs.2024.14953.1013 | ||
| نویسندگان | ||
| B. Mohamed Harif1؛ Karthikeyan M* 2؛ K. Perarasan3 | ||
| 1PG and Research Department of Mathematics, Rajah Serfoji Government College (Autonomous), Affiliated to Bharathidasan University, Thanjavur, India-613005. | ||
| 2Research Scholars, PG and Research Department of Mathematics, Rajah Serfoji Government College (Autonomous), Thanjavur-05 Tamilnadu, India. | ||
| 33Assistant Professor, PG and Research Department of Mathematics, Annai Vailankanni Arts and Science College, Thanjavur -07, Tamilnadu, India. | ||
| چکیده | ||
| This paper contributes to modeling and forecasting gas booking demand in an online retail environment using time series techniques. Our work demonstrates how historical demand data can be utilized to estimate future demand and its impact on the supply chain. The historical demand data were used to create several autoregressive integrated moving average (ARIMA) models using the Box-Jenkins time series procedure. The best model was selected based on four performance criteria: statistical results, maximum likelihood, and standard error. The selected model, ARIMA (1, 1, 1), was validated using additional historical demand data under the same conditions. The results demonstrate that the model can effectively estimate and forecast future demand for gas booking in an online retail environment. These findings will provide trustworthy guidance to the company's management in decision-making. | ||
| کلیدواژه‌ها | ||
| Fuzzy Time Series؛ Online Retail؛ Python؛ ARIMA | ||
| مراجع | ||
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[1] J. D. Wisner, K. C. Tan, and K. Leong, Principles of supply chain management: A balanced approach. South-Western, Cengage Learning, 2021. [2] D. Lu, Fundamentals of Supply Chain Management, Bookboon, 2011. [3] S. Shen and Y. Shen, ARIMA model in the application of Shanghai and Shenzhen stock index, Applied Mathematics, 7(3)2016, 171-176. [4] M. Matsumoto and A. Ikeda, Examination of demand forecasting by time series analysis for auto parts remanufacturing. Journal of Remanufacturing, 5(2015), 1-20. [5] A. A. Kurawarwala and H. Matsuo, Product Growth Models for Medium Term Forecasting of Short Life Cycle Products. IC2 Institute, 1992. [6] Willemain, T. R., Smart, C. N. and H. F. Schwarz, A new approach to forecasting intermittent demand for service parts inventories, International Journal of forecasting, 20(3)(2004), 375-387. [7] V. Kulshreshtha and N. K. Garg, Predicting the new cases of coronavirus [COVID-19] in India by using time series analysis as machine learning model in Python, Journal of The Institution of Engineers (India): Series B, 102(6)(2021), 1303-1309. [8] E. S. Karakoyun and A. O. Cibikdiken, Comparison of arima time series model and lstm deep learning algorithm for bitcoin price forecasting, In The 13th multidisciplinary academic conference in Prague, May 2018, 171-180. [9] I. M. Wirawan, T. Widiyaningtyas, and M. M. Hasan, Short term prediction on bitcoin price using ARIMA method, International Seminar on Application for Technology of Information and Communication (iSemantic), September 2019, 260-265. | ||
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