Forecasting the Number of Complaints with the Office of the Consumer Protection Board

Main Article Content

Wanrudee Suksanguan
Vadhana Jayathavaj


The Office of the Consumer Protection Board (OCPB) collects statistical data on consumer protection and analyzes trends and directions of consumer protection problems in various dimensions in order to supervise and follow up on concrete solutions to consumer protection problems. However, no advance predictions have been found yet. Changing trade patterns from traditional commerce to the e-commerce era and social media advertising that reaches consumers through smart devices will cause trade transactions to increase, resulting in an increase in the number of complaints submitted to the Office of the Consumer Protection Board (OCPB). This article aims to forecast the number of complaints for fiscal year 2023 using a time series model based on Gray System Theory, which can be used well with small amounts of data, for use with the number of complaints from fiscal year 2018 to 2022. The forecast is divided into four time series: real estate and housing, consumer products and services, and the total of the three groups above. The prediction results of the GM (1,1) expanded with periodic correction model (EPC) had the least Mean Absolute Percentage Error (MAPE), so the EPC model was chosen. It is predicted that fiscal year 2023 will have a number of complaints that increased or decreased from fiscal year 2022, as follows: Real estate group: 2,131 cases, a decrease of only 0.46 percent; consumer products group: 8,689 cases, an increase of 12.93 percent; service group: 6,193 cases, a decrease of 6.50 percent; and overall 17,244 cases, an increase of 4.78 percent. When combining the forecast values from the 3 groups, there will be a forecast value for the number of cases in fiscal year 2023 of 17,012 cases, which is only 232 cases less than the overall forecast of 17,244 cases, a decrease of only 1.34 percent.


Article Details

How to Cite
Suksanguan, W., & Jayathavaj, V. (2024). Forecasting the Number of Complaints with the Office of the Consumer Protection Board . Journal of Multidisciplinary in Humanities and Social Sciences, 7(2), 911–923. Retrieved from
Research Articles


พิชชากร เรืองเดชาวิวัฒน์. (2566, 4 พฤษภาคม). สคบ. คุ้มครองเราได้จริงไหม?: สิ่งที่ขาดหายไปในองค์กรคุ้มครองผู้บริโภค. สืบค้นเมื่อ 5 ธันวาคม 5, 2566, จาก

สำนักงานคณะกรรมการกิจการกระจายเสียง กิจการโทรทัศน์ และกิจการโทรคมนาคมแห่งชาติ (สำนักงาน กสทช.). (2567). อย่าหลงเชื่อ จะตกเป็นเหยื่อโฆษณาเกินจริง. สืบค้นเมื่อ 20 กุมภาพันธ์ 2567, จาก

สำนักงานคณะกรรมการกฤษฎีกา. (2562, 13 มิถุนายน). พระราชบัญญัติคุ้มครองผู้บริโภค พ.ศ. 2522. สืบค้นเมื่อ 20 กุมภาพันธ์ 2567, จาก

สำนักงานคณะกรรมการคุ้มครองผู้บริโภค. (2566). รายงานสถิติรับเรื่องร้องทุกข์ประจำปีงบประมาณ 2561 – 2565. สืบค้นเมื่อ 20 กุมภาพันธ์ 2567, จาก

Andrés, D. (2023). Machine Learning Pills: Error Metrics for Time Series Forecasting. Retrieved October 20, 2023, from

Businesswire. (2020, 29 October). China Express Delivery Industry Report, 2020-2024 with Profiles of S.F. Express, YTO Express, ZTO Express, Yunda Express and STO Express - Retrieved October 20, 2023, from https://www.businesswire .com/news/home/20201029005568/en/China-Express-Delivery-Industry-Report-2020-2024-with-Profiles-of-S.F.-Express-YTO-Express-ZTO-Express-

Contentserv. (2024). eCommerce vs. Digital Commerce: The future of shopping. Retrieved February 20, 2024, from

Deng, J. (2002). Grey Theory Basis. China: Huazhong University of Science and Technology Press of China. (in Chinese).

Deng, J. (1989). Introduction to Grey System Theory. The Journal of Grey System, 1(1), 1-24.

Javed, S.A. (2023). German Chancellor Merkel recognizes Grey System Theory. Retrieved February 20, 2024, from

Hyndman, R.J., & Athanasopoulos, G. (2021). Forecasting: principles and practice. (3rd ed). Melbourne: OTexts.

Khuman, A.S., Yang, Y., John, R., & Liu, S. (2016). R-fuzzy sets and grey system theory. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, Hungary, 2016, (pp. 004555-004560). doi:10.1109/SMC.2016.7844949

Lee, Y-S., & Tong, L-I. (2011). Forecasting energy consumption using a grey model improved by incorporating genetic programming. Energy Conversion and Management, 52, 147-152.

Leesa-nguansuk, S. (2023, 24 January). E-commece Projected to be grow by up to 13% this year. Bangkok Post. Retrieved February 20, 2024, from

Lewis, C.D. (1982). Industrial and business forecasting methods. London: Butterworths.

Lin, Y.H., Chiu, C.C., Lin, Y.J., Lee, P.C. (2013). Rainfall prediction using innovative grey model with the dynamic index. Journal of Marine Science and Technology, 21(1), 63-75.

Liu, S., & Lin, Y. (2010). Grey Systems Theory and Applications. Berlin Heidelberg: Springer-Verlag.

Liu, S., Zeng, F., Liu, J., & Xie, N. (2014). Several basic models of GM(1,1) and their applicable bound. Systems Engineering and Electronics, 36(3), 501-508.

Tang, S.Y., & Deng, G.M. (2015). Based on the Theory of Grey System to Forecast China’s Business Volume of Express Services. Modern Economy, 6, 283-288.

Yanqiu, C., Linjiang, Z., Jing, H., Zhe, Z., & Chunhui, L. (2020). Prediction of gas emission based on grey-generalized regression neural network. IOP Conference Series: Earth and Environmental Science, 467(1), 012056. doi:10.1088/1755-1315/467/1/012056

Yingjie, Y., & Liu, S. (2018). Grey systems, grey models and their roles in data analytics. Journal of Simulation: Systems, Science and Technology, 19(3), 1-6.

Zeng, B., Duan, H., & Zhou, Y. (2019). A new multivariable grey prediction model with structure compatibility, Applied Mathematical Modelling, 75, 385-397.

Zhang, Y. (2012). Improved grey derivative of grey Verhulst model and its application. International Journal of Computer Science, 9(6), 3, 443-448.

Zhou, W., & He, J-M. (2013). Generalized GM (1,1) model and its application in forecasting of fuel production, Applied Mathematical Modelling, 37(9), 6234-6243.