Forecasting food prices play an important role in livestock and agriculture to maximize profits and minimizing risks. An accurate food price prediction model can help the government which leads to optimization of resource allocation. This paper uses ridge regression as an approach for forecasting with many predictors that are related to the target variable. Ridge regression is an expansion of linear regression. It's fundamentally a regularization of the linear regression model. Ridge regression uses the damping factor (λ) as a scalar that should be learned, normally it will utilize a method called cross-validation to find the value. But in this research, we will calculate the damping factor/ridge regression in the ridge regression (RR) model firsthand to minimize the running time used when using cross-validation. The RR model will be used to forecast the food price time-series data. The proposed method shows that calculating the damping factor/regression estimator first results in a faster computation time compared to the regular RR model and also ANFIS.
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