Linear_SVM_Bank_9042_results.txt

fevd(x = aa, threshold = t, type = type_name, units = "Time", 
    time.units = "days", period.basis = "1 unit = 365 sample")

[1] "Estimation Method used: MLE"


 Negative Log-Likelihood Value:  560.8317 


 Estimated parameters:
      scale       shape 
23.10218313  0.00000001 

 Standard Error Estimates:
     scale      shape 
1.84835903 0.03928212 

 Estimated parameter covariance matrix.
           scale        shape
scale  3.4164311 -0.023171200
shape -0.0231712  0.001543085

 AIC = 1125.663 

 BIC = 1131.671 
[1] "the length of array 0 is 500"
[1] "threshold_0 is :  392.1"
[1] "num. items over threshold t :  149"
[1] "computation time 2.9599609375"
[1] "The mean and std=: 261.422"          "The mean and std=: 176.767247860004"
[1] "The error for mean and std: 7.90527164821045" "The error for mean and std: 5.58987118957137"
fevd(x = aa, threshold = t, type = type_name, units = "Time", 
    time.units = "days", period.basis = "1 unit = 365 sample")

[1] "Normal Approx."

                                                  95% lower CI Estimate 95% upper CI
1.36986301369863-1 unit = 365 sample return level     482.3308 507.7179     533.1050
2.73972602739726-1 unit = 365 sample return level     494.4570 523.7311     553.0053
5.47945205479452-1 unit = 365 sample return level     502.1719 539.7443     577.3168
13.6986301369863-1 unit = 365 sample return level     514.6646 560.9127     607.1607
27.3972602739726-1 unit = 365 sample return level     521.9401 576.9259     631.9116
54.7945205479452-1 unit = 365 sample return level     527.5950 592.9391     658.2832
136.986301369863-1 unit = 365 sample return level     536.6698 614.1074     691.5450

fevd(x = aa, threshold = t, type = type_name, units = "Time", 
    time.units = "days", period.basis = "1 unit = 365 sample")

[1] "Normal Approx."

                                                  95% lower CI Estimate 95% upper CI
1.36986301369863-1 unit = 365 sample return level     482.3308 507.7179     533.1050
2.73972602739726-1 unit = 365 sample return level     494.4570 523.7311     553.0053
5.47945205479452-1 unit = 365 sample return level     502.1719 539.7443     577.3168
13.6986301369863-1 unit = 365 sample return level     514.6646 560.9127     607.1607
27.3972602739726-1 unit = 365 sample return level     521.9401 576.9259     631.9116
54.7945205479452-1 unit = 365 sample return level     527.5950 592.9391     658.2832
136.986301369863-1 unit = 365 sample return level     536.6698 614.1074     691.5450

> print("Bayesian Factor Method:")
[1] "Bayesian Factor Method:"
> print(ind)
[1] 134
> print(max_index)
[1] 42
> print(max_observed)
[1] 636
> print("--------------------------")
[1] "--------------------------"
> print("Actual observed Time:")
[1] "Actual observed Time:"
> # what are actual timing after these steps:
> for(i in c(500, 1000, 2000, 3000, 4000, 5000, 10000))
+ {
+   print(i)
+   print(max(a$V1[1:i], na.rm = TRUE))
+ }
[1] 500
[1] 636
[1] 1000
[1] 636
[1] 2000
[1] 636
[1] 3000
[1] 654
[1] 4000
[1] 654
[1] 5000
[1] 654
[1] 10000
[1] 654