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

Mutwiri, Robert Mathenge

doi:10.11648/j.ijsd.20190503.11

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…The findings highlighted the relatively high prediction accuracy of GSM. R. Mutwiri (2019) Анотація. Побудова часових рядів з використання історичних даних є однією з актуальних проблем управління в аграрному секторі, оскільки аналіз і прогнозування процесів, пов'язаних з продовольчою безпекою держави, регіону, суб' єктів господарювання має вирішальне значення.…”

Section: Resultsunclassified

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2023

Scientific Horizons

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Свідоцтво про державну реєстрацію друкованого засобу масової інформації серії КВ № 24997-14937 ПР Науковий журнал включено до категорії А Переліку наукових фахових видань України, у яких можуть публікуватися результати дисертаційних робіт на здобуття наукових ступенів доктора та кандидата ветеринарних, економічних, сільськогосподарських і технічних наук зі спеціальностей: 051 -Економіка; 071 -Облік і оподаткування; 072 -Фінанси, банківська справа та страхування; 073 -Менеджмент; 075 -Маркетинг;

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Section: Resultsunclassified

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2023

Scientific Horizons

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“…In related works, Reddy [13] suggested a seasonal autoregressive integrated moving average (SARIMA) model anticipates tomato prices during harvest, which showed that prices fluctuated widely, indicating inadequate tomato econometric model forecasting capacity. Mutwiri [14] used the SARIMA model to analyze tomato price changes in Kenya. He found that the SARIMA(2,1,1)(1,0,1)12 model best predicts tomato prices in Nairobi Country, Kenya.…”

Section: Related Workmentioning

confidence: 99%

Hybrid approach for vegetable price forecasting in electronic commerce platform

Choong,

Sudin,

A. Raof

et al. 2024

IJ-AI

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<span lang="EN-US">The significance of the agriculture sector in Malaysia is often overlooked, and there is a notable deficiency in the advancement of digitalization within the country's agricultural domain. The integration of a price forecasting model in the platform enables the relevant parties, including farmers, to make informed decisions and plan their crop selection based on projected future prices. In this research, the authors proposed the hybrid approach with the combination of linear model and non-linear model in doing the vegetable price forecasting model. The hybrid SARIMA-DWT-GANN model is utilized to forecast the monthly vegetable prices in Malaysia. The historical vegetable price data is collected from the FAMA Malaysia and split into training/test set for modelling. The performance of the models is evaluated on the accuracy metrics including MAE, MAPE, and RMSE. The forecasted results using the proposed hybrid model are compared to that using the single SARIMA model. In conclusion, the hybrid SARIMA-DWT-GANN model is superior to the individual model, which obtained the smaller MAE, RMSE, and got the forecast accuracy of at least 95%. </span>

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“…The findings highlighted the relatively high prediction accuracy of GSM. R. Mutwiri (2019) (2022) compared the predictive accuracy of various models, including stacking (STACK), gradient boosting regression (GBR), extreme gradient boosting regression (XGBR), and random forest regression (RFR) ensembles. Additionally, it evaluates the performance of multilayer perceptron neural networks (MLP), extreme learning machines (ELM), and support vector regression (SVR) as reference models for forecasting demand.…”

Section: Table 5 Forecast Monthly Values Of the Number Of Cattle And ...mentioning

confidence: 99%

Forecasting husbandry development using time series

Kulyk,

Fokina-Mezentseva,

Piankova

et al. 2023

Scientific Horizons

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Building time series models based on historical data is a pressing challenge in the agricultural sector. This is essential, as analysing and predicting processes related to the food security of the state, region, and business entities are of paramount importance in management. With the help of forecasts, enterprises can adjust their production activities in such a way as to satisfy demand and deliver products to consumers on time. The research aims to predict the trends in the growth of cattle and cow populations and identify the most suitable forecasting timeframe. Statistical methods related to autoregression are used for this type of analysis: autoregressive models, moving average models or a combination of both, integrated variable structure models, and models that include seasonal effects and exogenous factors with an autoregressive and moving average component in the model. Monthly statistical data on the number of cattle and cows are used, among them mean, standard deviation, minimum and maximum values, asymmetry, and kurtosis. The dynamics of the decrease in the number of cattle and cows are shown. The studied series were checked for stationarity. The time series data for the cattle population underwent a Box-Cox transformation. The optimal parameters of the models used are given. Predictive values for periods (months) were obtained and the change in the number of cattle over the last 15 years was analysed. Constructed time series are compared with the actual values, which are illustrated in the graphs. Estimates of rootmean-square deviation, and mean absolute percentage error for different forecasting terms are given. By comparing these estimates for different time intervals, the optimal period for the forecast (24 months) was determined. This study allows farms and enterprises in the industry to predict a possible number of products (milk, meat) that could be collected or obtained in the future. It helps to take the necessary management steps: plan resource needs, improve efficiency, increase profits, reduce costs, and adapt to changes in the market

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

Cited by 11 publications

References 6 publications

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Hybrid approach for vegetable price forecasting in electronic commerce platform

Forecasting husbandry development using time series

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