Modeling of Tomato Prices in Ashanti Region, Ghana, Using Seasonal Autoregressive Integrated Moving Average Model

Boateng, Francis Ohene; Amoah-Mensah, John; Martin, Andrew; Osei, Lot; Dzebre, Paul

doi:10.9734/bjmcs/2017/30535

Cited by 5 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The model minimized the prediction errors were based on MAPE of 1.902, RMSE of 0.080, and MAE of 0.054. Reference [21] discovered that the SARIMA (0, 1, 1) (0,1,1)4 model was the best fit for tomato prices in Ghana's Ashanti region (BIC = 5.969). They observed significant price fluctuations in tomato prices throughout the year.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

Wanjuki

Wagala²,

Muriithi

2021

EJMATH

View full text Add to dashboard Cite

Price stability is the primary monetary policy objective in any economy since it protects the interests of both consumers and producers. As a result, forecasting is a common practice and a vital aspect of monetary policymaking. Future predictions guide monetary and fiscal policy tools that that be used to stabilize commodity prices. As a result, developing an accurate and precise forecasting model is critical. The current study fitted and forecasted the food and beverages price index (FBPI) in Kenya using seasonal autoregressive integrated moving average (SARIMA) models. Unlike other ARIMA models like the autoregressive (AR), Moving Average (MA), and non-seasonal ARMA models, the SARIMA model accounts for the seasonal component in a given time series data better forecasts. The study relied on secondary data obtained from the KNBS website on monthly food and beverage price index in Kenya from January 1991 to February 2020. R-statistical software was used to analyze the data. The parameter estimation was done using the Maximum Likelihood Estimation method. Competing SARIMA models were compared using the Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE),.and Mean Absolute Percentage Error (MAPE). A first-order differenced SARIMA (1,1,1) (0,1,1)12 minimized these model evaluation criteria (AIC = 1818.15, BIC =1833.40). The forecasting ability evaluation statistics MAE = 2.00%, MAPE = 1.62% and MASE = 0.87%. The 24-step ahead forecasts showed that the FPBI is unstable with an overall increasing trend. Therefore, the monetary policy committee ought to control inflation through monetary or fiscal policy, strengthening food security and trade liberalization.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

Wanjuki

Wagala²,

Muriithi

2021

EJMATH

View full text Add to dashboard Cite

show abstract

“…The efficiency of ARIMA and GARCH models were compared for modelling and forecasting the spot prices of Grams in India [23] and the SARIMA models were used to forecast the prices of Tomatoes in selected Indian states [19]. Gathondu [11] fitted four models to wholesale prices of major vegetables: tomato, potato, cabbages, kales and onions for markets in Nairobi, Mombasa, Kisumu, Eldoret and Nakuru in kenya using Autoregressive Moving Average (ARMA), Vector Autoregressive (VAR), Generalized Autoregressive Condition Heterostadicity (GARCH) and the mixed model of ARMA and GARCH.…”

Section: Review Of Previous Studiesmentioning

confidence: 99%

“…They reported that the highest tomatoe prices seasonally adjusted were in October. Boateng et al, [19] found that the predictability of the model increased with seasonal ARIMA (SARIMA). They noted wide fluctuations in prices of tomatoes in different months, prices sometimes increase 10 times compared to prices during peak harvest periods which implied that if farmers plan their area under tomatoes properly, sowing dates and sales by considering forecasted prices from the ARIMA models to receive increased prices, earnings may increase at least three to four times with 90% predictability of the forecast accuracy.…”

Section: Review Of Previous Studiesmentioning

confidence: 99%

Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model

Mutwiri¹

2019

IJSDA

View full text Add to dashboard Cite

Price forecasting is more sensitive with vegetable crops due to their high nature of perishability and seasonality and is often used to make better-informed decisions and to manage price risk. This is achievable if an appropriate model with high predictive accuracy is used. In this paper, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is developed to forecast price of tomatoes using monthly data for the period 1981 to 2013 obtained from the Ministry of Agriculture, Livestock and Fisheries (MALF) in the agribusiness department. Forecasting tomato prices was done using time series monthly average prices from January 2003 to December 2016. SARIMA (2, 1, 1) (1, 0, 1) 12 was identified as the best model. This was achieved by identifying the model with the least Akaike Information Criterion. The parameters were then estimated through the Maximum Likelihood Estimation method. The time series data of Tomatoes for wholesale markets in Nairobi are considered as the national average. The predictive ability tests RMSE = 32.063, MAPE = 125.251 and MAE = 22.3 showed that the model was appropriate for forecasting the price of tomatoes in Nairobi County, Kenya.

show abstract

“…En ese contexto, la necesidad de pronosticar el valor futuro de productos perecederos de corta vida de almacenamiento, es un objetivo principal en numerosas investigaciones alrededor del mundo (Boateng et al, 2017;Dharavath y Khosla, 2019;Paredes-García et al, 2019;Weng et al, 2019;Sabu y Kumar, 2020).…”

Section: Introductionunclassified

“…XXVII, No. especial 4 , 2021 ___________________________________________________________________197-212 Licencia de Creative Commons Atribución 4.0 Internacional (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/deed.es debido a que la desviación del pronóstico obtenido con respecto a los valores observados no es muy alta, se constituyen en herramienta confiables para proyectar precios a futuro de frutas y verduras (Boateng et al, 2017;Jadhav et al, 2017;Pardhi, Singh y Kumar, 2018;Dharavath y Khosla, 2019;Paredes-García et al, 2019;Weng et al, 2019).…”

Section: Introductionunclassified

Modelos de fluctuaciones de precios agrícolas: Estudio comparativo de frutas tropicales frescas en Colombia

2021

RCS

View full text Add to dashboard Cite

The cultivation of passion fruit (Passiflora Edulis Sims F. Purpurea and F. Flavicarpa) and granadilla (Passiflora Ligularis L.) are of great economic and productive interest in tropical countries, which is expressed by the sustained increase of the planting area to meet the growing demand. In order to minimize economic risks for investors, it is necessary to carry out adequate planning of production processes, estimating price fluctuations. The main objective of this article is to analyze price fluctuations, based on ARIMA (Autoregressive Integrated Moving Average) methods; unobserved components model and Markovian regression models, to establish the best performance through the minimization of the Akaike information criterion. To understand the stochastic phenomenon of the price time series, the data series from official statistics of these crops in an observation window between the years 2011-2019 were analyzed. The results showed that ARIMA models explain price fluctuations better and more accurately. It was concluded that this model is an ideal tool to analyze price behavior, contributing to price prediction, which is valuable information for decision makers.

show abstract

Modeling of Tomato Prices in Ashanti Region, Ghana, Using Seasonal Autoregressive Integrated Moving Average Model

Cited by 5 publications

References 0 publications

Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

Forecasting Commodity Price Index of Food and Beverages in Kenya Using Seasonal Autoregressive Integrated Moving Average (SARIMA) Models

Forecasting of Tomatoes Wholesale Prices of Nairobi in Kenya: Time Series Analysis Using Sarima Model

Modelos de fluctuaciones de precios agrícolas: Estudio comparativo de frutas tropicales frescas en Colombia

Contact Info

Product

Resources

About