Research on Seasonal Orderliness and Linear Correlation of Vegetable Sales Data

Zilu Wen; Xiangyue Zhang; Haoxu Miao

doi:10.54097/ckyeng26

Authors

Zilu Wen
Xiangyue Zhang
Haoxu Miao

DOI:

https://doi.org/10.54097/ckyeng26

Keywords:

Vegetable Marketing, Seasonality, Orderliness, Linear Correlation, Data Analysis

Abstract

The key to the study of seasonal orderliness and linear correlation of vegetable sales data is to understand how sales patterns are affected by seasonal factors and the linear relationship between these patterns and other variables. Seasonal ordering reflects cyclical changes in sales data over time that are influenced by factors such as climate, traditional festivals, and consumption habits. For example, certain vegetables may sell better in the winter while others in the summer. By analysing historical sales data, time series models such as Autoregressive Moving Average (ARMA) can be used to forecast future sales trends, which in turn support inventory management and pricing strategies. In addition, linear correlation analysis helps to identify the relationship between sales volume and factors such as price, cost and market demand, as well as the interactions between different vegetables, including complementary and substitution effects. This information is crucial for supply chain optimisation, promotional campaign planning and risk management. With the development of big data and analytics, researchers can more accurately model and predict market behaviours to support strategic decision-making for vegetable producers and retailers. Research that integrates seasonal ordering and linear correlation will provide insights into the complex dynamics of the vegetable market and provide data to support market participants in developing strategies to adapt to the changing environment.

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References

Pathan, S., Ndunguru, G., Islam, M. R., Jhumur, S. T., & Ayele, A. G. (2023). Production of Quinoa Leafy Greens in High Tunnel for Season Extension in Missouri. Horticulturae, 9(2), 209.

Borrero, J. D., & Borrero-Domínguez, J. D. (2023). Enhancing Short-Term Berry Yield Prediction for Small Growers Using a Novel Hybrid Machine Learning Model. Horticulturae, 9(5), 549.

Spiker, M. L., Welling, J., Hertenstein, D., Mishra, S., Mishra, K., Hurley, K. M., ... & Lee, B. Y. (2023). When increasing vegetable production may worsen food availability gaps: A simulation model in India. Food Policy, 116, 102416.

Chang, H. H., & Meyerhoefer, C. D. (2021). COVID‐19 and the demand for online food shopping services: Empirical Evidence from Taiwan. American Journal of Agricultural Economics, 103(2), 448-465.

Miliou, I., Xiong, X., Rinzivillo, S., Zhang, Q., Rossetti, G., Giannotti, F., ... & Vespignani, A. (2021). Predicting seasonal influenza using supermarket retail records. PLOS Computational Biology, 17(7), e1009087.

Trasberg, T., Soundararaj, B., & Cheshire, J. (2021). Using Wi-Fi probe requests from mobile phones to quantify the impact of pedestrian flows on retail turnover. Computers, Environment and Urban Systems, 87, 101601.

Falatouri, T., Darbanian, F., Brandtner, P., & Udokwu, C. (2022). Predictive analytics for demand forecasting–a comparison of SARIMA and LSTM in retail SCM. Procedia Computer Science, 200, 993-1003.

Sirisha, U. M., Belavagi, M. C., & Attigeri, G. (2022). Profit prediction using Arima, Sarima and LSTM models in time series forecasting: A Comparison. IEEE Access, 10, 124715-124727.

Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3, 100026.

Torres-Sánchez, R., Martínez-Zafra, M. T., Castillejo, N., Guillamón-Frutos, A., & Artés-Hernández, F. (2020). Real-time monitoring system for shelf life estimation of fruit and vegetables. Sensors, 20(7), 1860.

Dolega, L., Rowe, F., & Branagan, E. (2021). Going digital? The impact of social media marketing on retail website traffic, orders and sales. Journal of Retailing and Consumer Services, 60, 102501.

Vogel, C., Crozier, S., Penn-Newman, D., Ball, K., Moon, G., Lord, J., ... & Baird, J. (2021). Altering product placement to create a healthier layout in supermarkets: Outcomes on store sales, customer purchasing, and diet in a prospective matched controlled cluster study. PLoS Medicine, 18(9), e1003729.

Banerjee, T., Sinha, S., & Choudhury, P. (2022). Long term and short term forecasting of horticultural produce based on the LSTM network model. Applied Intelligence, 1-31.

Wudad, A., Naser, S., & Lameso, L. (2021). The impact of improved road networks on marketing of vegetables and households' income in Dedo district, Oromia regional state, Ethiopia. Heliyon, 7(10).

Adeagbo, O. A., & Adejumo, O. O. (2020). Economic analysis of dry season vegetable production in Ogun State, Nigeria. African Journal of Economic and Management Studies, 11(3), 427-441.

Li, Y., Zhou, H., Lin, Z., Wang, Y., Chen, S., Liu, C., ... & Xia, J. (2020). Investigation in the influences of public opinion indicators on vegetable prices by corpora construction and WeChat article analysis. Future Generation Computer Systems, 102, 876-888.

Stone, T. F., Thompson, J. R., Rosentrater, K. A., & Nair, A. (2021). A life cycle assessment approach for vegetables in large-, mid-, and small-scale food systems in the midwest US. Sustainability, 13(20), 11368.