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http://14.139.181.140:8080//jspui/handle/123456789/1935
Title: | From historical data to future predictions: Analyzing and forecasting oilseed yield trends in India using time series methods |
Authors: | Revathi, N Gourshetty, Dixitha Haritha, V Razak, Noorbina Arsha, Sugathan Sankar, M Thomas, Ashita Ragh, Aveen Mrunalini, Humbare Dinakar Venu, Vaisakh |
Keywords: | Oil seeds, time series analysis, ARIMA, ACF, PACF, AICc, RMSE |
Issue Date: | 2023 |
Publisher: | The Pharma Innovation Journal |
Abstract: | The study investigates the use of Autoregressive Integrated Moving Average (ARIMA) modeling techniques to predict oilseed yields using historical agricultural data. The dataset includes records of oilseed yields from multiple growing seasons in India. The study uses data preprocessing, cleaning, exploratory analysis, and rigorous stationarity checks. The ARIMA model's parameters are identified through Autocorrelation Function and Partial Autocorrelation Function plots. Performance is evaluated using Akaike Information Criterion corrected, Bayesian Information Criterion, Root Mean Squared Error, and residual analysis. The findings show the ARIMA model's effectiveness in capturing temporal patterns and seasonality in oilseed yield data, proving its ability to provide accurate forecasts. |
URI: | http://14.139.181.140:8080//jspui/handle/123456789/1935 |
ISSN: | 2277-7695 |
Appears in Collections: | Department of Basic Engineering and Applied Science |
Files in This Item:
File | Description | Size | Format | |
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BEAS ARTICLES.pdf | 1.01 MB | Adobe PDF | View/Open |
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