IEEE Computational Intelligence Magazine - November 2020 - 46
the value of the signal CC-IMF1 and TR at time t - 1 are
considered as features to be fed into the constructed RNN,
and the value of the signal CC-IMF1 at time t is considered as
the label or expected output of the RNN (Figure 13). The
size of the training set is equal to 80% of the number of elements of signal CC-IMF1, rounded to an integer (or equivalently the integer part of 80% of the number of elements of
signal TR). The same procedure is followed in all other cases.
5) an LSTM layer with 50 units.
6) a fully connected layer with one output unit.
The dropout layers were adapted to prevent over-fitting on
the training set. In fact, the dropout hyper-parameter indicates
the probability of training a given node in a layer. It has the regularisation effect and prevents over-fitting on the training set [31].
B. Construction of the Training Set and the Test Set
Consider signals CC-IMF1 and TR corresponding to Maharashtra. The proportion of 80% of data in both signals is used
in the training set and the remainder (20%) is considered as
the test set for validation purpose. To construct the training set,
Forecast
No. of Cases
No. of Cases
Adam optimisation has been set in options as the optimisation method to update network weights in each iteration,
Forecast
2,500
2,000
1,500
1,000
C. Setting the Options for the RNNs
0
2
4
6
10
8
12
14
16
1,800
1,600
1,400
18
0
RMSE = 176.8302
400
100
200
0
Error
Error
2,000
0
-200
-400
2
4
6
2
4
6
10
8
12
14
16
16
18
12
14
16
18
12
14
16
18
14
16
18
-100
18
0
2
4
6
10
8
Day
Day
(a)
(b)
Forecast
200
No. of Cases
No. of Cases
14
RMSE = 88.2194
Forecast
100
0
-100
-200
0
2
4
6
10
8
12
14
16
100
0
-100
-200
18
0
RMSE = 97.7989
200
2
4
6
10
8
RMSE = 101.7246
200
100
100
Error
Error
12
-200
0
200
0
-100
-200
10
8
0
-100
0
2
4
6
10
8
12
14
16
18
-200
0
2
4
6
10
8
Day
Day
(c)
(d)
Observed
12
Forecast
FIGURE 15 Predicted value and Root mean square error (RMSE) of the number of COVID-19 cases forecast corresponding to each IMF of Tamil
Nadu CC signal conducted on the test set. (a) CC; (b) CC-IMF1; (c) CC-IMF2; and (d) CC-IMF3.
46
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2020
IEEE Computational Intelligence Magazine - November 2020
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