Forecasting of Sweet Potato (Ipomoea batatas L.) Prices in India

Prakash, P.; Jaganathan, D.; Immanuel, Sheela; Lama, Achal; Sreekumar, J.; Sivakumar, P.

doi:10.48165/ijee.2022.58203

Cited by 6 publications

(2 citation statements)

References 10 publications

(11 reference statements)

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“…Talwar and Goyal (2019) employed exponential smoothing techniques, e.g., SES, DES, and HW, to forecast coriander prices. Prakash et al (2022) applied the HW approach to predict sweet potato 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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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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“…The current study attempted to predict Haryana's vegetable output in this context, providing the general public, academics, and decisionmakers with long-term information on state-wide vegetable production. According to the study of Prakash et al, (2022), to make informed decisions about sweet potato production, marketing, and consumption, sweet potato producers, governmental organisations, and other stakeholders in the sweet potato value chain will benefit greatly from the anticipated price of the crop. Nimbarayn et al, (2022) conducted a study on modelling and forecasting of area, production and productivity of tomatoes.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Vegetable Production in Haryana by Ordinary Least Square Method and ARIMA Model

Meena

Verma

2022

ijee

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India has the world’s second-largest production of fruit and vegetables. In India, a variety of agro-climate zones with unique seasons allow for the cultivation of a diverse range of vegetables. From this study, it was attempted to estimate the production of vegetables in the upcoming year through means of ordinary least squares (OLS) method and ARIMA (Autoregressive Integrated Moving Average) model using secondary data. This study is based on time series data of vegetables from 1990-91 to 2020-21 in open field condition, which was taken from National Horticulture Board (NHB) website, while the area and production reports on cultivation of vegetables from the Horticulture Department, Haryana state of India. In the current context, it was fetched out that the forecasted value of the vegetables production for the year 2021-22 will be 7540814.31 tonnes in open field condition. Further, for predicting the area and production of vegetables in Haryana, ARIMA (1, 2, 1) was fitted after experimenting with various lags of the moving average and autoregressive procedures.

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Forecasting Potato Production in Major South Asian Countries: a Comparative Study of Machine Learning and Time Series Models

Mishra,

Al khatib,

Mohamad Alshaib

et al. 2023

Potato Res.

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Forecasting of Sweet Potato (Ipomoea batatas L.) Prices in India

Cited by 6 publications

References 10 publications

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

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

Forecasting of Vegetable Production in Haryana by Ordinary Least Square Method and ARIMA Model

Forecasting Potato Production in Major South Asian Countries: a Comparative Study of Machine Learning and Time Series Models

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