The ARIMA Box-Jenkins Method has been used to Predict the Price of Large Curly Red Chilis

Septiani, Yustirania; Setyowati, Vinca Ayu

doi:10.32662/golder.v0i0.1516

Cited by 1 publication

(1 citation statement)

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“…The authors applied the ARIMA techniques to forecast the prices of rice (Adam, 2022;Fernando et al, 2021) and sugar (Şahinli, 2021). The researchers used this technique for forecasting chili (Septiani & Setyowati, 2021), palm oil (Yee & Humaida, 2021), and numerous other items (Zhou, 2021;Astiningrum et al, 2021;Taofik & Tiamiyu-Ibrahim, 2021) prices.…”

Section: Food Price Forecasting Using Several Approachesmentioning

confidence: 99%

An Extreme Learning Machine Approach for Forecasting the Wholesale Price Index of Food Products in India

Das,

Chakrabarti

2023

JST

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Precise food price forecasting is crucial for any country, and searching for appropriate approach(s) from an assortment of available strategies toward this objective is an open problem. The current Indian Wholesale Price Index (WPI) series contains sixty individual food items in the 'manufacture of food product' category. This work considered the monthly data from April 2011 to June 2022, i.e., one hundred thirty-five months' data of these sixty WPIs. The researchers extracted the linearity, curvature, and autocorrelation features for each WPI. The curvature and linearity-based grouping of these WPIs revealed that the WPIs are heterogeneous. This work proposed an extreme learning machine (ELM) approach for forecasting these WPIs. The present work employed the following twenty-two time-series forecasting techniques: six standard methods (Auto ARIMA, TSLM, SES, DES, TES, and Auto ETS), five neural networks (Auto FFNN, Auto GRNN, Auto MLP, Auto ELM, and proposed ELM), and eleven state-of-art techniques (two ARIMA-ETS based ensembles, an ARIMA-THETAF-TBATS based ensemble, one MLP, and seven LSTM-based models) to identify the best forecasting approach for these WPIs. For the majority of WPIs, the offered ELM attained suitable performance in the case of fifteen months of out-of-sample forecasting. Nearly eighty-seven percent of cases achieved high accuracy (MAPE ≤ ten) and outshined others. Upon accuracy comparison, both forecast-MAPE and forecast-RMSE, between the proposed ELM and others, this paper observed that the proposed ELM's performance is more favorable. This paper's findings imply that the proposed ELM is a promising prospect to offer accurate forecasts of these sixty WPIs.

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