Linear_SVM_Bank_36168_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:  542.7679 


 Estimated parameters:
       scale        shape 
1.409054e+02 5.289043e-04 

 Standard Error Estimates:
      scale       shape 
20.60043049  0.07580601 

 Estimated parameter covariance matrix.
            scale        shape
scale 424.3777364 -0.924555158
shape  -0.9245552  0.005746552

 AIC = 1089.536 

 BIC = 1094.601 
[1] "the length of array 0 is 194"
[1] "threshold_0 is :  1570.353"
[1] "num. items over threshold t :  93"
[1] "computation time 1.97509765625"
[1] "The mean and std=: 1068.62371134021" "The mean and std=: 759.829740596868"
[1] "The error for mean and std: 54.5525961421921" "The error for mean and std: 38.5745106634751"
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     2086.567 2343.640     2600.712
2.73972602739726-1 unit = 365 sample return level     2118.512 2441.609     2764.707
5.47945205479452-1 unit = 365 sample return level     2140.107 2539.615     2939.123
13.6986301369863-1 unit = 365 sample return level     2152.773 2669.226     3185.679
27.3972602739726-1 unit = 365 sample return level     2150.373 2767.315     3384.257
54.7945205479452-1 unit = 365 sample return level     2137.683 2865.440     3593.198
136.986301369863-1 unit = 365 sample return level     2105.151 2995.210     3885.269

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     2086.567 2343.640     2600.712
2.73972602739726-1 unit = 365 sample return level     2118.512 2441.609     2764.707
5.47945205479452-1 unit = 365 sample return level     2140.107 2539.615     2939.123
13.6986301369863-1 unit = 365 sample return level     2152.773 2669.226     3185.679
27.3972602739726-1 unit = 365 sample return level     2150.373 2767.315     3384.257
54.7945205479452-1 unit = 365 sample return level     2137.683 2865.440     3593.198
136.986301369863-1 unit = 365 sample return level     2105.151 2995.210     3885.269

[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] 1862
> 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] 2624
[1] 1000
[1] 2822
[1] 2000
[1] 3703
[1] 3000
[1] 3703
[1] 4000
[1] 3703
[1] 5000
[1] 3703
[1] 10000
[1] 3772