การพยากรณ์ราคารายเดือนของพืชสวนด้วยวิธีการพยากรณ์แบบเฉพาะและวิธีลำดับชั้นแบบบนลงล่าง

ศิรประภา ดีประดิษฐ์; ชนาธิป พรหมเพศ

Authors

ศิรประภา ดีประดิษฐ์ อาจารย์, มหาวิทยาลัยราชภัฏพระนครศรีอยุธยา เลขที่ 96 ถนนปรีดีพนมยงค์ ตำบลประตูชัย อำเภอพระนครศรีอยุธยา จังหวัดพระนครศรีอยุธยา 13000
ชนาธิป พรหมเพศ อาจารย์, มหาวิทยาลัยราชภัฏพระนครศรีอยุธยา เลขที่ 96 ถนนปรีดีพนมยงค์ ตำบลประตูชัย อำเภอพระนครศรีอยุธยา จังหวัดพระนครศรีอยุธยา 13000

Keywords:

horticulture, individual forecast, Top-Down hierarchical forecast

Abstract

Management in agricultural supply chains, the price factor is an important consideration since it influences the control of production costs. Being able to accurately predict the price in advance will result in more effective planning. The monthly forecasts of three types of horticultural crops, rubber, oil palm, and large dried coconut, were studied using an individual forecast and top-down hierarchical forecasting, and the forecasts were compared using the mean absolute error percentage and tracking signal. By dividing the data into 2 sets that are the training dataset which is monthly price data from 1999 to 2020 and the testing dataset, which is monthly price data in 2021, compared to actual price. The results showed that the rubber price was forecasted using a Damped Trend Non-Seasonal, oil palm, and large copra prices were based on top-down hierarchical forecasting based on the proper weighting. The mean absolute percentage error of the training data ranged from 6.25% to 7.03%. The comparison of the forecasted price and the real price in 2021, the mean absolute percentage error ranged from 8.91% to 9.55%. After analyzing the tracking signal values, the monthly prices were within the defined range [-6, +6]. Forecast Forecasted results are highly accurate forecasts. This work could be used as a planning tool by farmers and industrial enterprises.

References

Agricultural Research Development Agency. Horticulture [Internet]. [cited 2022 Jan 10]. Available from: https://www.arda.or.th

Agricultural Research Development Agency. New horticulture in Thailand [Internet]. [cited 2022 Jan 13]. Available from: https://www.arda.or.th

Wikipedia. Oil palm [Internet]. [cited 2022 Jan 13]. Available from: https://th.wikipedia.org/

Prasongsap S. Large dry coconut [Internet]. [cited 2022 Jan 13]. Available from: http://hort.ezathai.org/

Riansut W. Forecasting of coconut prices using the Box-Jenkins method. RMUTSB Academic Journal 2019;7(1):87-100. (In Thai)

Tanyarattanasrisakul M. The accuracy comparison of time series model between Winters’ exponential smoothing and Box - Jenkins methods: A case study of forecasting garden coconut price. RMUTSB Academic Journal 2018;6(2):101-13. (In Thai)

Noosen P, Payakkapong P, Supapakorn A. A comparison of quantity production of petroleum forecasting models in Thailand. Science and Technology Journal 2015;23(3): 376-84. (In Thai)

Luangtong N, Kantanantha N. Selection of the appropriate agricultural yield forecasting models. Science and Technology Journal 2015;24(3):370-81. (In Thai)

Deepradit S, Ruksorn P. The forecasting techniques comparison of field crops in Thailand. The Journal of Industrial Technology 2021;17(3):214-31. (In Thai)

Hyndman RJ, Ahmed RA, Athanasopoulos G, Shang HL. Optimal combination forecasts for hierarchical time series. Computational Statistics & Data Analysis 2011;55(9):2579-89.

Mirčetić D, Nikoličić S, Stojanović Đ, Maslarić M. Modified top down approach for hierarchical forecasting in a beverage supply chain. Transportation Research Procedia 2017;22:193-202.

Karmy JP, Maldonado S. Hierarchical time series forecasting via support vector regression in the European travel retail industry. Expert Systems with Applications 2019;137:59-73.

Deepradit S, Ongkunaruk P, Pisuchpen R. The study of forecasting techniques for aromatic coconut monthly prices using individual and hierarchical Forecasting. Thai Journal of Operations Research 2020;8(2):15-26. (In Thai)

Montaño J, Palmer A, Sesé A, Cajal B. Using the R-MAPE Index as a resistant measure of forecast accuracy. Psicothema 2013;25(4): 500-506.

Ongkunaruk P. Introduction to supply chain management for agro-industry. Bangkok: The One Printing; 2019. (In Thai)

Riansut W. Forecasting model for egg’s prices. Srinakharinwirot Research and Development Journal of Humanities and Social Sciences 2019;11(2):196-211. (In Thai)