Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal

Anisha Giri; Vijay Raj Giri

doi:doi:10.11648/j.aas.20230803.17

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Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal

Anisha Giri, Vijay Raj Giri

Published in Advances in Applied Sciences (Volume 8, Issue 3)

Received: 24 August 2023 Accepted: 11 September 2023 Published: 20 September 2023

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Abstract

Cucumber, originally indigenous to Southern Asia, thrives in diverse areas of Nepal. However, despite its promising prospects, cucumber cultivation in Nepal encounters obstacles resulting in reduced profits for farmers. These challenges encompass price volatility, marketing issues, the involvement of intermediaries in pricing, susceptibility to spoilage, and the substantial importation of cucumbers from neighboring India. Moreover, the agricultural market dynamics have led to traders shifting the burden of price risks onto farmers, culminating in lower returns for their produce. In that regard, this study focuses on the forecasting of cucumber prices in Nepal using time series analysis and compare the performance of two popular forecasting models: Seasonal Autoregressive Integrated Moving Average (SARIMA) and Simple Seasonal Exponential Smoothing (SSES). The objective is to provide accurate and reliable price predictions to assist stakeholders in making informed decisions in the cucumber market. The study utilizes historical cucumber price data spanning the past decade to understand the seasonal variations and trends in cucumber prices. The SARIMA model, known for its ability to capture seasonal effects, and the SSES model, a benchmark for seasonal time-series analysis, are both employed in the comparative assessment. The results reveal that the SSES model outperforms the SARIMA model in terms of forecasting accuracy, with lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values. The study's findings have significant implications for policymakers, researchers, and farmers involved in the cucumber market, offering valuable insights to optimize production and pricing strategies.

Published in	Advances in Applied Sciences (Volume 8, Issue 3)
DOI	10.11648/j.aas.20230803.17
Page(s)	106-121
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Econometric Analysis, Time Series, Seasonality Index, Box-Jenkins, Holt-Winters

References

[1]	Reddy, T. V. (2015). "Cucumber (Cucumis sativus L.): Nutritional content, phytochemistry, cultivation, and marketing." In: Watson, R. R., Preedy, V. R., Zibadi, S. (eds.) "Nuts and Seeds in Health and Disease Prevention." Academic Press.
[2]	Shrestha, R. M., Ghimire, K. L. (1996). "Vegetable Production in Nepal." In: Giri, G., Thapa, R. B. (eds.) "Prospects and Constraints in Vegetable Crops Research and Development in Nepal." Proceedings of the Second National Workshop. Nepal Agricultural Research Council.
[3]	Mukherjee, P. K., Nema, N. K., Maity, N., Sarkar, B. K. (2013). "Phytochemical and Therapeutic Potential of Cucumber." Fitoterapia, 84, 227-236.
[4]	MOAD. (2015) Agri-Business Promotion and Statistics Division. Ministry of Agriculture Development. Singhdurbar, Kathmandu: Government of Nepal.
[5]	MoF (2015) Nepal Portfolio Performance Review (NPPR). Ministry of finance. https://mof.gov.np/uploads/document/file/report_2015_20150914084119.pdf
[6]	MOALD (2019). "Statistical Information on Nepalese Agriculture 2018/19." Agri-Business Promotion and Statistics Division, Ministry of Agriculture and Livestock Development, Nepal.
[7]	Ghimire D. Lamsal G. Paudel B. Khatri S. Bhusal B. (2018) Analysis of Trend in Area, Production, and Yield of Major Vegetables of Nepal. Trends in Horticulture, 1: 1-11. DOI: 10.24294/th.v1i2.914.
[8]	Shrestha B. (2008) Off- Season Vegetables Marketing Channels of Small Growers: A Case of Yampaphant, Tanahun, Nepal. Wageningen, The Netherlands: Van Hall University of Applied Sciences. https://edepot.wur.nl/192
[9]	Karki Y. K. (2002) Agricultural Market and Marketing Information system of Vegetable Market in Kathmandu Valley. Kathmandu. Master dissertation. Institute of Agriculture and Animal Sciences, Rampur Campus, Chitwan, Nepal.
[10]	Alibuhtto, A., & Ariyarathna, T. (2019). Time series forecasting using ARIMA model. 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON), 1-6.
[11]	Adebiyi, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock price prediction using the ARIMA model. Journal of Emerging Trends in Computing and Information Sciences, 5 (4), 295-303. https://doi.org/10.1109/UKSim.2014.67
[12]	Esther, G. A., & Magdaline, C. (2017). Time series analysis of Indian stock market using ARIMA model. International Journal of Innovative Research in Science, Engineering and Technology, 6 (7), 12329-12334.
[13]	Kaur, H., & Ahuja, N. (2019). Time series forecasting using ARIMA and artificial neural network models: A comparative analysis. Proceedings of the 2019 2nd International Conference on Data, Engineering, and Applications (IDEA), 1-6.
[14]	Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. John Wiley & Sons.
[15]	Kihoro, J. M., Gachanja, P. M., & Orwa, J. O. (2004). Comparison of SARIMA and regression models in forecasting malaria time series. East African Agricultural and Forestry Journal, 70 (3), 235-247.
[16]	Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts.
[17]	Lima, S., Gonçalves, A. M., & Costa, M. (2019). Proceedings of the International Conference of Computational Methods in Sciences and Engineering (iccmse-2019), 2186, 090003-1–090003-4. New York: ALP Publishing.
[18]	Yu, Y., Sun, L., & Li, G. (2016). Proceedings of the 16th COTA International Conference of Transportation Professionals, 162–173. Reston: American Society of Civil Engineers.
[19]	Shahin, A. A. (2016). International Journal of Advanced Computer Science and Applications, 7, 91–96.
[20]	Kumari, R., & Tan, C. W. (2018). GARCH-based forecasting models: A review. Journal of Risk and Financial Management, 11 (3), 43.
[21]	Shetty, P., Rao, M., Acharya, D., Shetty, S., & Devi, K. K. (2018). Stock price prediction using GARCH models. International Journal of Engineering Research & Technology, 7 (2), 128-131.
[22]	Hamzacebi, C. (2008). Time series forecasting by neural network models. Neural Network World, 18 (2), 137-155.
[23]	Kamruzzaman, J., Sarker, R. A., & Hossain, M. A. (2006). Forecasting of daily rainfall using artificial neural network: A case study on Bangladesh. Paper presented at the International Conference on Computer and Communication Engineering, Kuala Lumpur, Malaysia.
[24]	AEC. (2006). Off-season Vegetables. Agro Enterprise Center/FNCCI, Kathmandu, Nepal.
[25]	Adanacioglu, H., & Yarcan, M. (2012). Modeling and forecasting of the monthly tomato price using SARIMA. Scientific Research and Essays, 7 (23), 2093-2100.
[26]	Luo, Z., Wang, Z., Wang, X., & Luo, Y. (2013). Forecasting cucumber price using SARIMA model. Chinese Agricultural Science Bulletin, 29 (10), 130-136.
[27]	Otu, E., Ismail, S. O., Odejimi, D. O., & Onyeagu, K. C. (2014). Forecasting the monthly percentage difference in the wholesale price index value in Nigeria using SARIMA. Journal of Statistical and Econometric Methods, 3 (4), 27-36.
[28]	Souza, J. P., Santana, C. C., & Alves, A. C. (2016). Forecasting monthly soybean prices using SARIMA models. Journal of Economics, Finance and Administrative Science, 21 (41), 121-133.
[29]	Bisht, M., & Kumar, R. (2019). Modeling and forecasting of price volatility in major pulses using GARCH model: A case of India. International Journal of Innovative Research in Science, Engineering and Technology, 8 (4), 2313-2320. https://doi.org/10.30954/0424-2513.3.2019.6
[30]	Vibas, K. C., & Raqueno, N. V. (2019). Forecasting movements of fruits and vegetable commodities in the National Capital Region using ARIMA, SARIMA, and ARIMAX models. International Journal of Management, Innovation and Entrepreneurial Research, 5 (2), 1-14.
[31]	Mutwiri, M., Gichuhi, R., & Wabwoba, F. (2019). Forecasting tomatoes wholesale price fluctuations in Nairobi using SARIMA. Journal of Economics and Sustainable Development, 10 (2), 38-50.
[32]	Divisekara, R. W., Jayasinghe, G. J. M. S. R., & Kumari, K. W. S. N. (2021). Forecasting the red lentils commodity market price using SARIMA model. SN Business & Economics, 1, 20. https://doi.org/10.1007/s43546-020-00020-x
[33]	Hyndman, R. J.; Khandakar, Y.: Automatic time series for forecasting: the forecast package for R. Department of Econometrics and Business Statistics, Monash University, Melbourne (2007).
[34]	Hyndman, R. J.; Koehler, A. B.; Snyder, R. D.; Grose, S. J. I. J.: A state space framework for automatic forecasting using exponential smoothing methods. Int. J. Forecast. 18 (3), 439–454 (2002).
[35]	Permanasari, A. E., Hidayah, I., & Bustoni, I. A. (2013, October). SARIMA (Seasonal ARIMA) implementation on time series to forecast the number of Malaria incidence. In 2013 International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 203- 207). IEEE.
[36]	Chen, K. Y., & Wang, C. H. (2007). A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan. Expert Systems with Applications, 32 (1), 254-264.
[37]	Bhar, R., & Sharma, P. (2007). A study of stationarity of Indian stock market series. International Research Journal of Finance and Economics, (11), 110-117.
[38]	Tebbs, C. (2011). Statistical methods for financial markets. John Wiley & Sons.
[39]	Shumway, R. H., & Stoffer, D. S. (2017). Time series analysis and its applications: with R examples. Springer.
[40]	Chatfield, C. (2016). Analysis of time series: an introduction. CRC Press. Singh, M., Singh, R., and Shinde, V. 2011. Application of Software Packages for Monthly Stream Flow Forecasting of Kangsabati River in India, International Journal of Computer Applications (0975 – 8887), Volume 20– No. 3, 7-14. [http://www.ijcaonline.org/volume20/number3/pxc3873231.pdf]
[41]	Republica. (2018, September 8). Vegetable prices remain constant despite government monitoring. https://myrepublica.nagariknetwork.com/news/vegetable-prices-remain-constant-despite-government-monitoring/
[42]	Kathmandu Post. (2020, March 28). Vegetables become scarce and costly as transporters stay away due to virus fears. The Kathmandu Post. https://kathmandupost.com/12/2020/03/28/vegetables-become-scarce-and-costly-as-transporters-stay-away-due-to-virus-fears
[43]	Singh, M., Singh, R., and Shinde, V. (2011). Application of Software Packages for Monthly Stream Flow Forecasting of Kangsabati River in India, International Journal of Computer Applications (0975 – 8887), Volume 20– No.3, 7-14. [http://www.ijcaonline.org/volume20/number3/pxc3873231.pdf]
[44]	Karki, S. (2019, September 27). Vegetables' price skyrockets as Dashain festival nears. Khabarhub. https://english.khabarhub.com/2019/27/46468/

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Anisha Giri, Vijay Raj Giri. (2023). Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal. Advances in Applied Sciences, 8(3), 106-121. https://doi.org/10.11648/j.aas.20230803.17

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Anisha Giri; Vijay Raj Giri. Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal. Adv. Appl. Sci. 2023, 8(3), 106-121. doi: 10.11648/j.aas.20230803.17

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AMA Style

Anisha Giri, Vijay Raj Giri. Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal. Adv Appl Sci. 2023;8(3):106-121. doi: 10.11648/j.aas.20230803.17

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@article{10.11648/j.aas.20230803.17,
  author = {Anisha Giri and Vijay Raj Giri},
  title = {Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal},
  journal = {Advances in Applied Sciences},
  volume = {8},
  number = {3},
  pages = {106-121},
  doi = {10.11648/j.aas.20230803.17},
  url = {https://doi.org/10.11648/j.aas.20230803.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aas.20230803.17},
  abstract = {Cucumber, originally indigenous to Southern Asia, thrives in diverse areas of Nepal. However, despite its promising prospects, cucumber cultivation in Nepal encounters obstacles resulting in reduced profits for farmers. These challenges encompass price volatility, marketing issues, the involvement of intermediaries in pricing, susceptibility to spoilage, and the substantial importation of cucumbers from neighboring India. Moreover, the agricultural market dynamics have led to traders shifting the burden of price risks onto farmers, culminating in lower returns for their produce. In that regard, this study focuses on the forecasting of cucumber prices in Nepal using time series analysis and compare the performance of two popular forecasting models: Seasonal Autoregressive Integrated Moving Average (SARIMA) and Simple Seasonal Exponential Smoothing (SSES). The objective is to provide accurate and reliable price predictions to assist stakeholders in making informed decisions in the cucumber market. The study utilizes historical cucumber price data spanning the past decade to understand the seasonal variations and trends in cucumber prices. The SARIMA model, known for its ability to capture seasonal effects, and the SSES model, a benchmark for seasonal time-series analysis, are both employed in the comparative assessment. The results reveal that the SSES model outperforms the SARIMA model in terms of forecasting accuracy, with lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values. The study's findings have significant implications for policymakers, researchers, and farmers involved in the cucumber market, offering valuable insights to optimize production and pricing strategies.},
 year = {2023}
}

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TY  - JOUR
T1  - Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal
AU  - Anisha Giri
AU  - Vijay Raj Giri
Y1  - 2023/09/20
PY  - 2023
N1  - https://doi.org/10.11648/j.aas.20230803.17
DO  - 10.11648/j.aas.20230803.17
T2  - Advances in Applied Sciences
JF  - Advances in Applied Sciences
JO  - Advances in Applied Sciences
SP  - 106
EP  - 121
PB  - Science Publishing Group
SN  - 2575-1514
UR  - https://doi.org/10.11648/j.aas.20230803.17
AB  - Cucumber, originally indigenous to Southern Asia, thrives in diverse areas of Nepal. However, despite its promising prospects, cucumber cultivation in Nepal encounters obstacles resulting in reduced profits for farmers. These challenges encompass price volatility, marketing issues, the involvement of intermediaries in pricing, susceptibility to spoilage, and the substantial importation of cucumbers from neighboring India. Moreover, the agricultural market dynamics have led to traders shifting the burden of price risks onto farmers, culminating in lower returns for their produce. In that regard, this study focuses on the forecasting of cucumber prices in Nepal using time series analysis and compare the performance of two popular forecasting models: Seasonal Autoregressive Integrated Moving Average (SARIMA) and Simple Seasonal Exponential Smoothing (SSES). The objective is to provide accurate and reliable price predictions to assist stakeholders in making informed decisions in the cucumber market. The study utilizes historical cucumber price data spanning the past decade to understand the seasonal variations and trends in cucumber prices. The SARIMA model, known for its ability to capture seasonal effects, and the SSES model, a benchmark for seasonal time-series analysis, are both employed in the comparative assessment. The results reveal that the SSES model outperforms the SARIMA model in terms of forecasting accuracy, with lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values. The study's findings have significant implications for policymakers, researchers, and farmers involved in the cucumber market, offering valuable insights to optimize production and pricing strategies.
VL  - 8
IS  - 3
ER  -

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Author Information

Anisha Giri

National Center for Potato, Vegetable and Spice Crops Development, Department of Agriculture, Ministry of Agriculture and Livestock Development, Government of Nepal, Kathmandu, Nepal
Vijay Raj Giri

Department of Mechanical Engineering, University of Michigan-Dearborn, Michigan, USA

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APA Style

Anisha Giri, Vijay Raj Giri. (2023). Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal. Advances in Applied Sciences, 8(3), 106-121. https://doi.org/10.11648/j.aas.20230803.17

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ACS Style

Anisha Giri; Vijay Raj Giri. Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal. Adv. Appl. Sci. 2023, 8(3), 106-121. doi: 10.11648/j.aas.20230803.17

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AMA Style

Anisha Giri, Vijay Raj Giri. Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal. Adv Appl Sci. 2023;8(3):106-121. doi: 10.11648/j.aas.20230803.17

Copy | Download

@article{10.11648/j.aas.20230803.17,
  author = {Anisha Giri and Vijay Raj Giri},
  title = {Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal},
  journal = {Advances in Applied Sciences},
  volume = {8},
  number = {3},
  pages = {106-121},
  doi = {10.11648/j.aas.20230803.17},
  url = {https://doi.org/10.11648/j.aas.20230803.17},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aas.20230803.17},
  abstract = {Cucumber, originally indigenous to Southern Asia, thrives in diverse areas of Nepal. However, despite its promising prospects, cucumber cultivation in Nepal encounters obstacles resulting in reduced profits for farmers. These challenges encompass price volatility, marketing issues, the involvement of intermediaries in pricing, susceptibility to spoilage, and the substantial importation of cucumbers from neighboring India. Moreover, the agricultural market dynamics have led to traders shifting the burden of price risks onto farmers, culminating in lower returns for their produce. In that regard, this study focuses on the forecasting of cucumber prices in Nepal using time series analysis and compare the performance of two popular forecasting models: Seasonal Autoregressive Integrated Moving Average (SARIMA) and Simple Seasonal Exponential Smoothing (SSES). The objective is to provide accurate and reliable price predictions to assist stakeholders in making informed decisions in the cucumber market. The study utilizes historical cucumber price data spanning the past decade to understand the seasonal variations and trends in cucumber prices. The SARIMA model, known for its ability to capture seasonal effects, and the SSES model, a benchmark for seasonal time-series analysis, are both employed in the comparative assessment. The results reveal that the SSES model outperforms the SARIMA model in terms of forecasting accuracy, with lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values. The study's findings have significant implications for policymakers, researchers, and farmers involved in the cucumber market, offering valuable insights to optimize production and pricing strategies.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Comparative Assessment of SARIMA and SSES Models for Forecasting Cucumber Prices in Nepal
AU  - Anisha Giri
AU  - Vijay Raj Giri
Y1  - 2023/09/20
PY  - 2023
N1  - https://doi.org/10.11648/j.aas.20230803.17
DO  - 10.11648/j.aas.20230803.17
T2  - Advances in Applied Sciences
JF  - Advances in Applied Sciences
JO  - Advances in Applied Sciences
SP  - 106
EP  - 121
PB  - Science Publishing Group
SN  - 2575-1514
UR  - https://doi.org/10.11648/j.aas.20230803.17
AB  - Cucumber, originally indigenous to Southern Asia, thrives in diverse areas of Nepal. However, despite its promising prospects, cucumber cultivation in Nepal encounters obstacles resulting in reduced profits for farmers. These challenges encompass price volatility, marketing issues, the involvement of intermediaries in pricing, susceptibility to spoilage, and the substantial importation of cucumbers from neighboring India. Moreover, the agricultural market dynamics have led to traders shifting the burden of price risks onto farmers, culminating in lower returns for their produce. In that regard, this study focuses on the forecasting of cucumber prices in Nepal using time series analysis and compare the performance of two popular forecasting models: Seasonal Autoregressive Integrated Moving Average (SARIMA) and Simple Seasonal Exponential Smoothing (SSES). The objective is to provide accurate and reliable price predictions to assist stakeholders in making informed decisions in the cucumber market. The study utilizes historical cucumber price data spanning the past decade to understand the seasonal variations and trends in cucumber prices. The SARIMA model, known for its ability to capture seasonal effects, and the SSES model, a benchmark for seasonal time-series analysis, are both employed in the comparative assessment. The results reveal that the SSES model outperforms the SARIMA model in terms of forecasting accuracy, with lower Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) values. The study's findings have significant implications for policymakers, researchers, and farmers involved in the cucumber market, offering valuable insights to optimize production and pricing strategies.
VL  - 8
IS  - 3
ER  -

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