Forecast Analysis of Yearly Groundnut Productivity in India Using Auto Regressive Integrated Moving Averages model

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B.R. Sreedhar

Abstract

This study paper is an observation of the productivity and production of peanuts in India. Data from 1966-67 to 2019-20 were analyzed using time collection methods. For the construction and forecasting of the versions from 1966-67 to 2017-18. The statistics from 2018-19 to 2019-20 are used for the validation of the versions. For statistics, the automatic correlation function (ACF) and the partial automatic correlation function (PACF) were calculated. The regressive integrated moving average version is rising.The validity of the version is examined using well-known statistical techniques. The overall version performance is demonstrated by means of an evaluation with a percentage deviation from values ​​and suggests an absolute mean percentage error (MAPE). For the forecast item, automatic regressive production The Integrated Moving Averages (0,1,1) and Integrated Moving Averages Auto Regressive versions (0,1,1) were used respectively to forecast certain key years. respectively of hectares with decrease and 10.3718 hectares of higher restriction lakh, the production foresees respectively about 6.4445 heaps of lakh with decrease restriction and 8.6487 lakh of higher restriction. The rising sample is tested by means of becoming an exponential, linear function.The end result confirmed that the linearly increasing charge compound

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