IEEE Computational Intelligence Magazine - November 2020 - 42
1) Signals CC-IMF1 (Figure 7(a)), TR (Figure 2(d)), and
H-IMF1 (Figure 11(a)) are trend non-stationary and, therefore, are used to train a separate RNN.
2) Signals CC-IMF2 (Figure 7(b)), T-IMF2 (Figure 10(b))
and H-IMF2 (Figure 11(b)) are stationary and have rela-
tively similar center frequencies of 0.1264, 0.1312, and
0.0887 respectively and, therefore, are used to train a separate RNN.
3) Signals CC-IMF3 (Figure 7(c)), T-IMF3 (Figure 10(c)) and
H-IMF 3 (Figure 11(c)) are stationary and have similar
95
36.5
36
Temperature (°C)
Humidity (%)
90
85
80
75
70
0
10
20
30
40 50
Day
(a)
60
70
80
Temperature (°C)
Humidity (%)
0
20
40
Day
(a)
60
80
1
0
-5
-10
0
-1
-2
-15
0
10
20
30
40 50
Day
(b)
60
70
80
-3
90
15
0
10
20
30
40 50
Day
(b)
60
70
80
90
0
10
20
30
40 50
Day
(c)
60
70
80
90
2
1.5
Temperature (°C)
10
Humidity (%)
34
2
5
5
0
-5
-10
1
0.5
0
-0.5
-1
-1.5
0
10
20
30
40 50
Day
(c)
60
70
80
90
FIGURE 9 IMFs corresponding to the decomposition of humidity (H)
time series of Maharashtra along with their center frequencies,
(a) H-IMF1, ~ 1 = 0.0001. (b) H-IMF2, ~ 2 = 0.1858. (c) H-IMF3,
~ 3 = 0.3860.
42
34.5
33
90
10
-15
35
33.5
15
-20
35.5
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2020
-2
FIGURE 10 IMFs corresponding to the decomposition of temperature
(T) time series of Tamil Nadu along with their center frequencies,
(a) T-IMF1, ~ 1 = 9 # 10 -6 . (b) T-IMF2, ~ 2 = 0.1312. (c) T-IMF3,
~ 3 = 0.3804.
IEEE Computational Intelligence Magazine - November 2020
Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - November 2020
Contents
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