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Table 2 (a) The RMSE, MAE and ICC statistics of the used methods for prediction of ILI; (b) the performance criteria of the used methods for prediction of ILI outbreaks

From: Comparative evaluation of time series models for predicting influenza outbreaks: application of influenza-like illness data from sentinel sites of healthcare centers in Iran

(a)

Model

Kernel

 

Criterion

RMSE

MAE

ICC

RFTS

Train

25.3

6.43

0.92

Test

22.78

14.99

0.88

SVM

RBF

Train

58.71

14.3

0.58

Test

28.19

22.36

0.53

Polynomial

Train

55.20

15.00

0.53

Test

239.00

91.20

0.09

Linear

Train

53.60

13.00

0.53

Test

30.10

18.60

0.47

Sigmoid

Train

63.90

17.30

0.43

Test

30.80

20.00

0.24

ANN

Train

37.50

11.94

0.84

Test

26.58

13.21

0.82

ARIMA

Train

47.01

17.92

0.64

Test

34.90

28.16

0.03

(b)

Model

Sensitivity

Specificity

Criterion

PPVa

NPVb

Total accuracy

RF

 Train

1.000

1.000

1.000

1.000

1.000

 Test

0.804

0.964

0.974

0.750

0.865

SVM

 Train

1.000

1.000

1.000

1.000

1.000

 Test

0.848

0.964

0.975

0.794

0.892

ANN

 Train

0.962

0.940

0.828

0.977

0.948

 Test

0.862

0.904

0.833

0.922

0.889

  1. aPositive predictive value
  2. bNegative predictive value