Page 10 - Azerbaijan State University of Economics

THE JOURNAL OF ECONOMIC SCIENCES: THEORY AND PRACTICE, V.80, # 1, 2023, pp. 4-20

As such, since tuning the parameters affects the resulting value, suitable values for the
parameters were obtained through a grid search approach within a set boundary while
the overall structure remained fixed.

In this research, the ReLU activation was used as it was, proven to be the most effective.
Furthermore, in order to reduce overfitting and improve the performance of the model,
the dropout and recurrent dropout settings were each set to 0.1. The epochs were set to
100, with an early stopping function with a patience setting of 10 put in place in order
to make sure the loss function output did not increase during the training.

Next, setting the number of units as 8, 16, 32, the learning rate as 0.01, 0.05, 0.1, and
batch size as 16, 32, 48 as variables, all possible combinations were attempted. The
result of which was that out of the 26 possible combinations, for Period 1, when the
parameters were unit 16, learning 0.001, batch size 16, the RMSE was minimized, and
for Period 2, when the parameters were unit 16, learning rate 0.05, batch size 32, the
RMSE was similarly minimized. The selected parameters were used to build the
model for each time period. Figure 3 is a graph comparing actual and expected values
of agricultural growth outcomes.
Figure 3. Var forecasting

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