Forecasting Prices of Black Pepper in Kerala and Karnataka using Univariate and Multivariate Recurrent Neural Networks
subject: Black Pepper; Gated Recurrent Unit (GRU); Long Short-Term Memory (LSTM); Macroeconomic; Singular Spectral Analysis (SSA); Time series
description: Our country has a high level of agricultural employment. Price swings harm the economy of our country. To combat this impact, forecasting the selling price of agricultural products has become a need. Forecasts of agricultural prices assist farmers, government officials, businesses, central banks, policymakers, and consumers. Price prediction can then assist in making better selections in this area. Black pepper, sometimes known as the "King of Spices, " is a popular spice farmed and exported in India. The largest producers of black pepper are Karnataka and Kerala. For black pepper in Kerala and Karnataka, this study provides a univariate and multivariate price prediction model using Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data is denoised using Singular Spectral Analysis (SSA). The most accurate method is the multivariate variate LSTM technique, which uses macroeconomic variables. It has a Mean Absolute Percentage Error (MAPE) of 0.012 and 0.040 for Kerala and Karnataka, respectively. Grenze Scientific Society, 2022.
creator: Kiranjith K.A.; Pramila R.M.
source: 13th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2022, Vol-8, pp. 803-810.
publisher: Grenze Scientific Society
date: 2022-01-01
rights: Restricted Access
relation: ISBN: 978-171385793-8
format: Online
language: English
type: Conference paper
coverage: Kiranjith K.A., Christ (Deemed to be University), Pune Lavasa Campus, Maharashtra, India; Pramila R.M., Christ (Deemed to be University), Pune Lavasa Campus, Maharashtra, India