Is it possible to predict food retail prices? Evidence from Lithuanian market

Volkov, Artiom; Morkūnas, Mangirdas; Skvarciany, Viktorija

doi:10.3846/cibmee.2019.018

Cited by 3 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Asnhari et al (2019) utilized it for red chili, onion, and garlic price prediction. Volkov et al (2019) applied it to predict the butter, egg, and bread 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

View full text Add to dashboard Cite

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.

show abstract

“…Asnhari et al (2019) utilized it for red chili, onion, and garlic price prediction. Volkov et al (2019) applied it to predict the butter, egg, and bread 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

View full text Add to dashboard Cite

show abstract

Forecast Model Development of Some Selected Wholesale Price Index of India Using MLP

Das

Chakrabarti

2021

Lecture Notes on Data Engineering and Communications Technologies

View full text Add to dashboard Cite

Methodological Approaches to Forecasting Bread Prices in Ukraine

Lobotska

Pavlov

Didukh

et al. 2021

Scientific Horizons

View full text Add to dashboard Cite

The article examines the current state of the bread and bakery market in Ukraine on the basis of the exponential smoothing method. An important aspect of the analysis of the bakery industry state is the issue of pricing for the number one product in Ukraine – bread and bakery products. The purpose of this study was to analyze the level of bread prices in the regional context, to identify trends and factors influencing them and to propose models on the basis of which it is advisable to make operational forecasts of bread prices. The study was performed on the basis of monitoring data of average consumer prices for wheat bread from first grade flour by months of 2017 and 2018 in the selected regions, Kyiv and Ukraine as a whole. The choice of areas is done due to their territorial location, and the choice of bread type – due to steady popularity among consumers. The dynamics of product prices, in particular in the regional aspect, was analyzed. The example of wheat bread made from first grade flour shows significant differences in the price level for these products by regions. Trends in price changes and their dependence on such factors as the price of flour, the price of gasoline A-95, wages have been identified. The expediency of using for the estimation of bread prices of models based on series of dynamics by exponential smoothing is shown. High accuracy of the received models is confirmed. The proposed approach in this study can be used by industry to construct models of product price forecasting as a benchmark for making management decisions about the real price. Performing these calculations online on a computer will provide businesses with particular advantages over their competitors, as well as the ability to plan their economic performance at the desired level

show abstract

Is it possible to predict food retail prices? Evidence from Lithuanian market

Cited by 3 publications

References 35 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

Forecast Model Development of Some Selected Wholesale Price Index of India Using MLP

Methodological Approaches to Forecasting Bread Prices in Ukraine

Contact Info

Product

Resources

About