An intelligent Crop Price Prediction using suitable Machine Learning Algorithm

Ishita, Ghutake; Verma, Ritesh; Rohit, Chaudhari; Vidhate, Amarsinh

doi:10.1051/itmconf/20214003040

Cited by 16 publications

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, predictor-searching analysis deals with the seeking of patterns of interrelations among dependent and independent variables. Recent applications of such methods to crop price estimation include the usage of regression-based decision trees and random forests (4) , multiple linear regression (5) , and cross-sectional and panel regression modeling (6) . However, regression-based models assume that errors are not correlated, a characteristic not exhibited by timeseries data.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Farmgate Rice Price Using Temporal Causal Modeling and Kalman Filtering

Patacsil¹,

Cenas²,

Jacobe³

et al. 2022

IJST

View full text Add to dashboard Cite

Objectives:To develop a forecasting model for the farmgate prices of rice crop in the Philippines and to improve the derived model forecasts by applying Kalman filters. Methods: The researcher's utilized monthly rice farmgate price and inflation rate from 1990 to 2015 as training information in building the temporal-causal model. On the other hand, the dataset for rice farmgate price from 2016 to 2020 acted as the testing set, allowing the researchers to determine model accuracy using mean square error, mean absolute error, and mean absolute percentage error. Findings: Results indicate that applying Kalman filters to the derived temporal-causal model indeed improves prediction performance, as evidenced by the lower MSE values. In particular, applying Kalman filter to the derived Temporal-Causal model 15% (without inflation as control input) and 3% (with inflation as control input) decrease in the MSE. In terms of the Temporal-Causal-Kalman filter with no control input, a decrease of 1.8% is observed for the MAE as well as a decrease of 3% in the MAPE, indicating a substantial improvement in the accuracy of the base model. Interestingly though, adding a control-input variable in the Kalman filter generated gave an increase of 4.4% and 3.8% in the MAE and MAPE respectively. This might be due to the not-so-strong correlation between the farmgate price and control input (inflation). Seeking conditions when will external inputs be helpful in enhancing Kalman filters as well as combining the models with other data analytics techniques may be valuable in future research. As for the comparison with nonlinear setups, results for unscaled, partially scaled, and fully scaled artificial neural networks show that Kalman filtering can attain almost on-par prediction performance with such methods. Novelty: This research presented a new scheme of predicting farmgate price. Compared to typical time series models, the derived Temporal-Causal model included inflation as a factor. Moreover, the combination of Temporal-Causal and Kalman filter is a new method for improving forecasts.

show abstract

Section: Introductionmentioning

confidence: 99%