Fig 1.
Various categories of nature-inspired meta-heuristic algorithms.
Fig 2.
Flowchart describing the proposed model for predicting stock prices using LS-SVM optimized by ADA.
Table 1.
Parameter values of DA.
Table 2.
Values of the fitness function for different algorithms run on various companies’ datasets.
Fig 3.
Predicted stock prices of 12 companies using 8 different algorithms.
(a) Adobe, (b) American Express, (c) Apple, (d) AT&T, (e) Bank of New York, (f) Coca-Cola, (g) ExxonMobil, (h) FMC, (i) HP, (j) Honeywell, (k) Oracle, (l) Tesla.
Table 3.
Values of the fitness function for linear kernel LS-SVM algorithm run on various companies’ datasets.
Table 4.
Optimization results of the 8 different algorithms including ADA on 12 benchmark functions.
Table 5.
p-values of the Wilcoxon rank-sum test in terms of MSE of the proposed method on stock market datasets.
Table 6.
p-values of the Wilcoxon rank-sum test in terms of average of benchmark functions.