Potato Price Forecasting with Holt-Winters and ARIMA Methods: A Case Study

Şahinli, Mehmet Arif

doi:10.1007/s12230-020-09788-y

Cited by 27 publications

(15 citation statements)

References 18 publications

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“…T he H WA m odel, o n t he o ther hand, worked best in the past for forecasting sales [37]. However, recent studies [38] showed that the ARIMA models did much better than the HWA for forecasting and hence, it has been one of the more popular. In 2018, Taylor et al proposed a model for predicting time series [31], the FP Model.…”

Section: Discussionmentioning

confidence: 99%

Predicting COVID-19 infections and deaths in Bangladesh using Machine Learning Algorithms

Leon

Iqbal

Azim

et al. 2021

2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD)

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Section: Discussionmentioning

confidence: 99%

Predicting COVID-19 infections and deaths in Bangladesh using Machine Learning Algorithms

Leon

Iqbal

Azim

et al. 2021

2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD)

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“…It is possible to quantify the degree of similarity between the values of the series at neighboring points with the help of such a function. That is, it is the usual Pearson correlation coefficient [37] between the values of a given time series and its copy shifted by a certain number of values. The partial autocorrelation function is the autocorrelation of the residuals of the previous order autoregressive.…”

Section: Finding Initial Approximations For the Predictive Modelmentioning

confidence: 99%

Analysis of the Forecast Price as a Factor of Sustainable Development of Agriculture

et al. 2021

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Analysis of the rise in prices for consumer goods is a state’s priority task. The state assumes the obligation to regulate pricing in all spheres of consumption. First of all, the prices for essential commodities to which agricultural products belong are analyzed. The article shows the changes in prices for consumer goods of agricultural products (sugar) during a pandemic. The analysis of forecasting prices for sugar and its impact on the development of its production is carried out. The construction of the forecast model was based on extrapolation. The structure of a forecast model for price changes was based on the analysis of the time series of the Autoregressive Integrated Moving Average (ARIMA) class. This model consists of an autoregressive model and a moving average model. A forecast of the volume of domestic sugar transportation by rail has been completed. The algorithms implemented this model for searching for initial approximations and optimal parameters for the predictive model. The Hirotsugu Akaike Information Criterion (AIC) was used to select the best model. The algorithms were implemented in the Python programming language. The quality check of the description was performed with a predictive model of actual data. An economic interpretation of the rise in sugar prices and proof of the forecast’s truth obtained from a financial point of view were carried out.

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“…In production forecasting of Thailand's crude palm oil, Suppalakpanya et al [5]studied various ES techniques, observed that additive Holt-Winters and extended additive Holt-Winters exhibited the smallest MAPE. To forecast the price of potatoes in Turkey, Şahinli [6]used ARIMA, Holt-Winters additive, and Holt-Winters multiplicative approaches and found the ARIMA model with p=1, d=1, and q=2 is the best. Rasheed et al [21] forecast the PKR's exchange rate using the ES technique and obtained MAPE=6.53.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this work, we employed widely accepted and used TS forecasting approaches to forecast and compared their performances with the proposed MLP. For each WPINonLinear, we developed the following models:  Regression [31,32]: Linear (L1), Quadratic (Q), Cubic (C), Logarithmic (L2), and Exponential (E)  Exponential smoothing [5,6]: Holt's linear trend (H1), Holt's exponential trend (H2), and Holt-Winters (HW)  Auto ARIMA (A) [33,34]  SVR [10,14] We used R's stats package to build the regression models [29]. To develop the exponential smoothing and automatic ARIMA models, we employed R software's forecast package [35,36].…”

Section: 6forecast Model Building For the Wpinonlinear Using Other Approaches (Step6)mentioning

confidence: 99%

Forecasting non-linear WPI of manufacture of chemicals and chemical products in India: an MLP approach

Das¹,

Chakrabarti²

2021

IJATEE

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Forecasting predicts/ estimates the future by taking into account historical data. It's an instrument of decision-making that assists companies/ enterprises/ institutions in managing uncertainties. It allows the business to set up goals and create a budget. Forecasting helps us to anticipate changes and therefore guide us towards data-driven strategies and reasoned decisions/ choices. It serves as a proactive rather than a reactive measure. There are several forecasting approaches for the univariate time series (TS) data, e.g. *Author for correspondence Auto-Regressive Integrated Moving Average (ARIMA) [1-3], exponential smoothing (ES) [4-6], Support Vector Regression (SVR) [7, 8, 9], neural approaches [10-12]. The recent studies show that the researchers employed these approaches for forecasting various TS data, e.g., stock prices [1, 2, 13], prices of agricultural products [4, 5, 6], price index [10, 14].India's chemical industry is highly diverse, covers a wide range of products, and has a large import and export base. More than two million people are working here. On the economic growth of India, this sector's contribution is notable. In the future, there exists a significant opportunity for the industry's business/ revenue growth.

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Potato Price Forecasting with Holt-Winters and ARIMA Methods: A Case Study

Cited by 27 publications

References 18 publications

Predicting COVID-19 infections and deaths in Bangladesh using Machine Learning Algorithms

Predicting COVID-19 infections and deaths in Bangladesh using Machine Learning Algorithms

Analysis of the Forecast Price as a Factor of Sustainable Development of Agriculture

Forecasting non-linear WPI of manufacture of chemicals and chemical products in India: an MLP approach

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