Linear_SVM_Bank_27126_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:  424.0521 


 Estimated parameters:
      scale       shape 
58.89162041  0.09615694 

 Standard Error Estimates:
     scale      shape 
7.97721764 0.07904671 

 Estimated parameter covariance matrix.
           scale        shape
scale 63.6360013 -0.292407127
shape -0.2924071  0.006248382

 AIC = 852.1042 

 BIC = 856.9177 
[1] "the length of array 0 is 194"
[1] "threshold_0 is :  1110.845"
[1] "num. items over threshold t :  82"
[1] "computation time 3.541015625"
[1] "The mean and std=: 739.927835051546" "The mean and std=: 527.750658624713"
[1] "The error for mean and std: 37.8902891075497" "The error for mean and std: 26.7924803690672"
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     1357.003 1523.252     1689.501
2.73972602739726-1 unit = 365 sample return level     1373.622 1593.888     1814.153
5.47945205479452-1 unit = 365 sample return level     1383.234 1669.392     1955.549
13.6986301369863-1 unit = 365 sample return level     1383.395 1777.248     2171.101
27.3972602739726-1 unit = 365 sample return level     1372.525 1865.389     2358.254
54.7945205479452-1 unit = 365 sample return level     1350.752 1959.606     2568.459
136.986301369863-1 unit = 365 sample return level     1302.769 2094.192     2885.615

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     1357.003 1523.252     1689.501
2.73972602739726-1 unit = 365 sample return level     1373.622 1593.888     1814.153
5.47945205479452-1 unit = 365 sample return level     1383.234 1669.392     1955.549
13.6986301369863-1 unit = 365 sample return level     1383.395 1777.248     2171.101
27.3972602739726-1 unit = 365 sample return level     1372.525 1865.389     2358.254
54.7945205479452-1 unit = 365 sample return level     1350.752 1959.606     2568.459
136.986301369863-1 unit = 365 sample return level     1302.769 2094.192     2885.615

[1] "--------------------------"
There were 50 or more warnings (use warnings() to see the first 50)
> 
> 
> print("Bayesian Factor Method:")
[1] "Bayesian Factor Method:"
> print(ind)
[1] 94
> print(max_index)
[1] 2
> print(max_observed)
[1] 1276
> 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] 2089
[1] 1000
[1] 2089
[1] 2000
[1] 2089
[1] 3000
[1] 2118
[1] 4000
[1] 2319
[1] 5000
[1] 2319
[1] 10000
[1] 2515