Linear_SVM_Bank_44758_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:  514.8569 


 Estimated parameters:
       scale        shape 
2.206004e+02 1.287048e-04 

 Standard Error Estimates:
      scale       shape 
35.15436106  0.08394056 

 Estimated parameter covariance matrix.
            scale        shape
scale 1235.829102 -1.797313923
shape   -1.797314  0.007046017

 AIC = 1033.714 

 BIC = 1038.527 
[1] "the length of array 0 is 193"
[1] "threshold_0 is :  2066.198"
[1] "num. items over threshold t :  82"
[1] "computation time 2.06689453125"
[1] "The mean and std=: 1322.01554404145" "The mean and std=: 1035.4058876064" 
[1] "The error for mean and std: 74.5301466317017" "The error for mean and std: 52.700772086104" 
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     2830.507 3248.874     3667.242
2.73972602739726-1 unit = 365 sample return level     2873.020 3401.895     3930.770
5.47945205479452-1 unit = 365 sample return level     2897.542 3554.930     4212.317
13.6986301369863-1 unit = 365 sample return level     2902.404 3757.251     4612.098
27.3972602739726-1 unit = 365 sample return level     2885.326 3910.317     4935.308
54.7945205479452-1 unit = 365 sample return level     2850.445 4063.397     5276.350
136.986301369863-1 unit = 365 sample return level     2777.107 4265.779     5754.450

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     2830.507 3248.874     3667.242
2.73972602739726-1 unit = 365 sample return level     2873.020 3401.895     3930.770
5.47945205479452-1 unit = 365 sample return level     2897.542 3554.930     4212.317
13.6986301369863-1 unit = 365 sample return level     2902.404 3757.251     4612.098
27.3972602739726-1 unit = 365 sample return level     2885.326 3910.317     4935.308
54.7945205479452-1 unit = 365 sample return level     2850.445 4063.397     5276.350
136.986301369863-1 unit = 365 sample return level     2777.107 4265.779     5754.450

[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] 193
> print(max_index)
[1] 101
> print(max_observed)
[1] 3515
> 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] 3515
[1] 1000
[1] 3515
[1] 2000
[1] 4378
[1] 3000
[1] 4378
[1] 4000
[1] 4378
[1] 5000
[1] 4378
[1] 10000
[1] 4378