Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
In this study, Time series forecasting is used to predict the cost of tomatoes considering data from previous
four years, this will be done using Autoregressive Integrated Moving Average (ARIMA) approach. Using this
integrated Autoregressive and Moving Average approach yields better prediction results rather than an
individual method. Augmented dickey fuller test is conducted to check stationarity and then Arima model is
trained based on the dataset to predict the price of tomato of next 5 months. The prediction is done for months
June to October with a error score RMSE of 8.31 which yielded satisfactory results.
Keywords:
Forecasting, ARIMA, Supply Chain, Fresh produce
Cite Article:
"Time Series Forecasting for a Fresh Produce Product in Supply Chain using ARIMA", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 7, page no.1684 - 1689, July-2022, Available :http://www.ijrti.org/papers/IJRTI2207290.pdf
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000204884
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator