Linear_SVM_Bank_18084_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:  629.6842 


 Estimated parameters:
      scale       shape 
31.92337531  0.09957808 

 Standard Error Estimates:
     scale      shape 
3.30885150 0.05975546 

 Estimated parameter covariance matrix.
            scale        shape
scale 10.94849825 -0.088633280
shape -0.08863328  0.003570715

 AIC = 1263.368 

 BIC = 1269.223 
[1] "the length of array 0 is 368"
[1] "threshold_0 is :  747.079"
[1] "num. items over threshold t :  138"
[1] "computation time 4.10595703125"
[1] "The mean and std=: 477.051630434783" "The mean and std=: 352.350700802535"
[1] "The error for mean and std: 18.36754998011"   "The error for mean and std: 12.9878191447186"
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     899.9982  966.3957     1032.793
2.73972602739726-1 unit = 365 sample return level     916.7023 1004.9772     1093.252
5.47945205479452-1 unit = 365 sample return level     931.2330 1046.3157     1161.398
13.6986301369863-1 unit = 365 sample return level     946.4212 1105.5303     1264.639
27.3972602739726-1 unit = 365 sample return level     954.2931 1154.0543     1353.816
54.7945205479452-1 unit = 365 sample return level     958.4940 1206.0460     1453.598
136.986301369863-1 unit = 365 sample return level     957.4511 1280.5203     1603.589

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     899.9982  966.3957     1032.793
2.73972602739726-1 unit = 365 sample return level     916.7023 1004.9772     1093.252
5.47945205479452-1 unit = 365 sample return level     931.2330 1046.3157     1161.398
13.6986301369863-1 unit = 365 sample return level     946.4212 1105.5303     1264.639
27.3972602739726-1 unit = 365 sample return level     954.2931 1154.0543     1353.816
54.7945205479452-1 unit = 365 sample return level     958.4940 1206.0460     1453.598
136.986301369863-1 unit = 365 sample return level     957.4511 1280.5203     1603.589

[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] 168
> print(max_index)
[1] 76
> print(max_observed)
[1] 819
> 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] 1249
[1] 1000
[1] 1249
[1] 2000
[1] 1249
[1] 3000
[1] 1260
[1] 4000
[1] 1260
[1] 5000
[1] 1283
[1] 10000
[1] 1357