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Unfolding the potential of the ARIMA model in forecasting maize production in Tanzania

Joseph Lwaho & Bahati Ilembo

Business Analyst Journal2023https://doi.org/10.1108/baj-07-2023-0055article
ABDC C
Weight
0.52

What the paper says

Purpose This paper was set to develop a model for forecasting maize production in Tanzania using the autoregressive integrated moving average (ARIMA) approach. The aim is to forecast future production of maize for the next 10 years to help identify the population at risk of food insecurity and quantify the anticipated maize shortage. Design/methodology/approach Annual historical data on maize production (hg/ha) from 1961 to 2021 obtained from the FAOSTAT database were used. The ARIMA method is a robust framework for forecasting time-series data with non-seasonal components. The model was selected based on the Akaike Information Criteria corrected (AICc) minimum values and maximum log-likelihood. Model adequacy was checked using plots of residuals and the Ljung-Box test. Findings The results suggest that ARIMA (1,1,1) is the most suitable model to forecast maize production in Tanzania. The selected model proved efficient in forecasting maize production in the coming years and is recommended for application. Originality/value The study used partially processed secondary data to fit for Time series analysis using ARIMA (1,1,1) and hence reliable and conclusive results.

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Cite this paper

https://doi.org/https://doi.org/10.1108/baj-07-2023-0055

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@article{joseph2023,
  title        = {{Unfolding the potential of the ARIMA model in forecasting maize production in Tanzania}},
  author       = {Joseph Lwaho & Bahati Ilembo},
  journal      = {Business Analyst Journal},
  year         = {2023},
  doi          = {https://doi.org/https://doi.org/10.1108/baj-07-2023-0055},
}

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Evidence weight

0.52

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.43 × 0.4 = 0.17
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.