1 [email protected] [email protected]

PCA
[email protected]
[email protected]
1
Modeling Forecasting Inflation by Artificial Neural Network
Combination model with PCA
Abstract:
This paper concerning researches are done about reason of deflation in Iran suggest forecasting model
for inflation and compare it with ARIMA.
Inflation is into annual and use of CPI index. Recently, there has been considerable interest in
applications of neural networks in the economics Literature but In contrast, relatively few studies have
applied neural network methods to macroeconomic time series. So we try forecast this macroeconomic
variable precisely by enter factor that reflate.
Variable that derive of previous studies are: liquidity, exchange rate, interest rate, Imported good and
previous inflation for Expected Rate of Inflation. Conclusions indicate that neural network forecast
precisely and entrance effective variables on inflation improve accurate forecasting.
Keyword: forecasting/inflation/artificial neural network/liquidity/interest rate/imported goods index/
Expected Rate of Inflation/PCA
2
ARIMA
CPI
ARIMA
MSE
AR(1)
C45,C53,E31,E37 JEL
/
3
[1]
[12,13]
[14,15]
ARIMA
STRS NNRS
NNRS
[16]
CPI
ARIMA
1
2
James
nakamura
Neural Network Regime Switching
4
Smoot-Transition Regime Switching
5
In sample performance
6
Out-of-Sample performance
7
Customer price index
3
4
[2]
[3]
[4]
5
[5]
[17]
ARIMA
artificial neural network
Mackey-Glass Equation
Methods Threshold AR Model
Time-Varying Parameter
1
2
3
Expected Rate of Inflation
threshold autoregressive
time-varying
6
[9]
[10]
f
b
W
.
P
a
feed forward
1
Classify
Cluster
3
feed forward
4
recurrent
2
7
[6]
a
MSE
w
t
[-1,1]
pn = 2*(p-minp)/(maxp-minp) – 1
p
pn
[18]
[-1,1] [0,1]
[19]
1
Back Propagation
Imported good
3
normalization
4
Component principle analysis
2
8
feed forward
tansig
MSE,RMSE,MAE,MAPE
ARIMA
AR(1)
Q
-5.50626
-3.73785
-2.99188
-2.63554
ARIMA
.
1
Kolmogorov Theorem
Unit Root Test
3
Auto Correlation
4
Partial Auto Correlation
2
9
2
p
(d p
zp)
p 1
1 MSE ( MeanSquaredError )
p
2
p
(d p
zp)
p 1
2 RMSE ( RootMeanSquaredError )
p
2
p
(d p
2
3 R (CofficientOfDeter min ation) 1
zp)
p 1
2
p
(d p
dp)
p 1
p
dp
4 MAE ( MeanAbsoluteError )
zp
p 1
p
5 MAPE ( MeanAbsolutePercentageError )
100
P
p
p 1
dp
zp
dp
R
AR(1)
.
10
MSE
RMSE
MAE
MAPE
AR(1)
ARIMA
ARIMA
60
50
40
30
20
10
0
1
spss13
3
5
7
9
11
EWiews
13
15
17
19
21
23
25
27
29
31
33
35
AR(1)
MATLAB7
Minitab13
MSE
AR(1)
11
–
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and application. PD.Idea Group pub.
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neural network. College of engineering and technology of ohio university.pp 1-23.
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Economics.pp 293-335.
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How bottom is bottom?.Economic Modelling.pp1-13.
- Emi Nakamura.(2005). Inflation forecasting using a neural network. Economics Letters 1
86.pp 373-378
16- Paul D. M C Nelis-Amsterdam.(2005).neural network in finance :Gaining predictive Edge
in the market . Elsevier Academic press.pp167-197.
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time series forecasting.Department of Industrial and Management Systems Engineering
Morgantown, West Virginia.pp 1-61.
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Transaction on System,Man, and Cybernetic;665-685.
19-Kim Yong Seog,(2004),An intelligent system for targeting:a data mining approach.J
Decision Support System;215-228.
12
VAR00001
27
N
Normal Parameters(a,b)
Most Extreme
Differences
Mean
1.5214
Std. Deviation
6.58598
Absolute
.132
Positive
.132
Negative
-.097
Kolmogorov-Smirnov Z
.685
Asymp. Sig. (2-tailed)
.737
a Test distribution is Normal.
b Calculated from data.
AR(1)
VAR00001
N
Normal Parameters(a,b)
Most
Extreme
Differences
27
Mean
2.2130
Std. Deviation
9.65110
Absolute
.104
Positive
.072
Negative
-.104
Kolmogorov-Smirnov Z
.539
Asymp. Sig. (2-tailed)
.933
a Test distribution is Normal.
b Calculated from data
13
1350
9.091
296.30
76.0
9.5
9.5
1.1
1351
9.09091
399.4
69.0
9.5
9.5
1.2
1352
8.33333
517.5
68.0
9.5
9.5
1.3
1353
15.3846
813.7
68.0
9.5
9.5
1.5
1354
6.66667
1149.5
68.0
9.5
9.5
1.6
1355
18.75
1625.7
71.0
10.5
10
1.7
1356
26.3158
2139.4
71.0
10.5
10.5
1.9
1357
8.33333
2578.6
100.0
10.5
10.5
2.1
1358
11.5385
3628.3
141.0
6
8
2.4
1359
24.1379
4508.1
200.0
6
8
2.9
1360
1361
22.2222
20.4545
5236.1
6430.7
270.0
350.0
6
6
8
8
3.2
3.5
1362
1363
1364
13.2075
11.6667
5.97015
7514.4
7966.9
9002.1
450.0
580.0
614.0
6
10
10
8
8
8
3.7
3.8
4.1
1365
23.9437
10722.6
742.0
10
8
5.3
1366
28.4091
12668.2
991.0
10
8
7.1
1367
28.3186
15687.6
1019.0
10
8
8.8
1368
17.931
18753.3
1207.0
10
8
10
1369
9
22969.5
1412.0
14
12
13.4
1370
20.7
28628.4
1420.0
14
12
16.2
1371
24.4
35866.0
1498.0
14
13
22.5
1372
22.9
48135.0
1806.0
14
17
28.7
1373
35.2
61843.9
2667.0
15
17
42.2
1374
49.4
85072.2
4036.0
14.5
18
72.5
1375
23.2
116552.6
4446.0
14.5
18
93.6
1376
17.3
134286.3
4782.0
14.5
18
100
1377
18.1
160401.5
6468.0
15.5
18
110
1378
20.1
192689.2
8658.0
15.5
18
134.2
1379
12.6
249110.7
8188.0
15.5
18
152.1
1380
11.4
320957.3
8008.0
15.5
17
153.3
1381
1382
1383
15.8
15.6311
15.2393
417524.0
526596.4
685697.5
8019.0
8323.0
8747.0
14.5
15
15
16
16
15
159.7
167.3
191.5
1384
12.0583
921019.4
9042.0
15
16
204.2
1385
15.3
1284199.4
9244.0
13
14
226.2536
14