Food price index prediction using time series models: A study of Cereals, Millets and Pulses

Majhi, Santosh Kumar; Bano, Rabiyanaz; Srichan, Suresh Kumar; Acharya, Biswaranjan; Al-Rasheed, Amal; Alqahtani, Mohammed S.; Abbas, Mohamed; Soufiene, Ben Othman

doi:10.21203/rs.3.rs-2999898/v1

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…The integration of ARIMA forecasting in millet trade analysis aligns with its success in other agricultural markets and underscores its potential to provide actionable insights for stakeholders and decision-makers. By leveraging the capabilities of ARIMA models, the millet industry can enhance its strategic planning and decision-making processes, capitalizing on evolving market (Majhi, et al, 2023)…”

Section: Expanding the Application Of Arima Forecasting In Millets Tr...mentioning

confidence: 99%

Arima Forecasting In Soymeal And Beyond: An Empirical Study Bridging The Path To Millet-Based Forecasting

Shashank Mishra, Vaishanavee Vartak

2023

jier

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Soymeal, a rich source of protein and fiber, held paramount importance in livestock and poultry sectors. Derived post oil extraction from soybean seeds, its production and price dynamics hinged on multiple factors including seed supply-demand, crushing costs, transportation expenses, and competition from alternative feed constituents. The study aimed to employ the Autoregressive Integrated Moving Average (ARIMA) model to forecast soymeal production, import, export, and price trends globally for year 2023 by using data from 2016 to 2022. By combining historical insights with predictive analytics, the paper endeavored to illuminate the prospective trajectory of soymeal dynamics on a global scale, offering valuable insights for stakeholders. Moreover, it is noteworthy that the applicability of the ARIMA forecasting model extends beyond soymeal, encompassing other agricultural commodities such as millets and other minor grains. Millets, celebrated for their nutritional value and resilience, hold immense potential in addressing global food security challenges. The ARIMA model's capability to unravel temporal intricacies and patterns could likewise provide insights into millet production, pricing, and trade, contributing to the sustainable development of diverse agricultural sectors. This research not only enriches our understanding of soymeal dynamics but also underscores the versatility of the ARIMA model for forecasting a spectrum of agricultural commodities, promoting informed decision-making and strategic planning.

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Section: Expanding the Application Of Arima Forecasting In Millets Tr...mentioning

confidence: 99%

Arima Forecasting In Soymeal And Beyond: An Empirical Study Bridging The Path To Millet-Based Forecasting

Shashank Mishra, Vaishanavee Vartak

2023

jier

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A hybrid SARIMA-Prophet model for predicting historical streamflow time-series of the Sobat River in South Sudan

Kenyi,

Yamamoto

2024

Discov Appl Sci

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Accurate river streamflow forecasting is pivotal for effective water resource planning, infrastructure design, utilization, optimization, and flood planning and warning. Streamflow prediction remains a difficult task due to several factors such as climate change, topography, and lack of observed data in some cases. This paper investigates and evaluates the individual performances of the seasonal auto-regressive integrated moving average (SARIMA) and Prophet models in forecasting the streamflow of the Sobat River and proposes a hybrid SARIMA-Prophet model to leverage the strengths of both approaches. Using the augmented Dickey-Fuller (ADF) and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests, the flow of the Sobat River was found to be stationary. The performance of the models was then assessed based on their residual errors and predictive accuracy using the mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R2). Residual analysis and prediction capabilities revealed that Prophet slightly edged SARIMA in terms of prediction efficacy; however, both models struggled to effectively capture extreme values, resulting in significant overestimations and slight underestimations. The hybrid SARIMA-Prophet model significantly reduced residual variability, achieving a lower MAE of 4.047 m3/s, RMSE of 6.17 m3/s, and a higher R2 of 0.92 than did the SARIMA (MAE: 5.39 m3/s, RMSE: 8.70 m3/s, R2: 0.85) and Prophet (MAE: 5.35 m3/s, RMSE: 8.32 m3/s, and R2: 0.86) models. This indicates that the hybrid model handles both long-term patterns and short-term fluctuations more effectively than the individual models. The findings of the present study highlight the potential of hybrid SARIMA-Prophet models for streamflow forecasting in terms of accuracy and reliability, thus contributing to more effective water resource management and planning, particularly in the Sobat River.

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Forecasting cauliflower prices in Nepal: a comparative analysis using seasonal time series and nonlinear models

Giri,

Giri

2024

Cogent Food & Agriculture

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Food price index prediction using time series models: A study of Cereals, Millets and Pulses

Cited by 4 publications

References 19 publications

Arima Forecasting In Soymeal And Beyond: An Empirical Study Bridging The Path To Millet-Based Forecasting

Arima Forecasting In Soymeal And Beyond: An Empirical Study Bridging The Path To Millet-Based Forecasting

A hybrid SARIMA-Prophet model for predicting historical streamflow time-series of the Sobat River in South Sudan

Forecasting cauliflower prices in Nepal: a comparative analysis using seasonal time series and nonlinear models

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