Sustainability 13 12789 v2
Sustainability 13 12789 v2
Sustainability 13 12789 v2
Article
A Regional Industrial Economic Forecasting Model Based on a
Deep Convolutional Neural Network and Big Data
Shouheng Tuo 1,2,3, * , Tianrui Chen 1 , Hong He 4, *, Zengyu Feng 1 , Yanling Zhu 1 , Fan Liu 1 and Chao Li 1
1 School of Computer Science and Technology, Xi’an University of Posts and Telecommunications,
Xi’an 710121, China; ctr1498@126.com (T.C.); fengzengyu@stu.xupt.edu.cn (Z.F.);
zhuyanling11@126.com (Y.Z.); l_nlxx@163.com (F.L.); lichao8849@163.com (C.L.)
2 Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an 710121, China
3 Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an 710121, China
4 College of Economics and Management, Xi’an University of Posts & Telecommunications, Xi’an 710121, China
* Correspondence: tuo_sh@126.com (S.T.); hehongtbw@126.com (H.H.)
Abstract: To accurately predict the economic development of each industry in different types of
regions, a deep convolutional neural network model was designed for predicting the annual GDP;
GDP growth index; and primary, secondary and tertiary industry growth values of each. In the model,
raw industrial data are preprocessed by a normalization operation and subsequently transformed
by the BoxCox method to approach the normal distribution. Panel data of consecutive years are
constructed and used as input to the deep convolutional neural network, and industrial data of year
t + 1 are used as the output of the network. Simulation experiments were conducted to analyze
23 years of industrial economic data from 31 provinces, municipalities, and autonomous regions in
China. The experimental results show that R-squared value is larger than 0.91 for all 31 provinces
Citation: Tuo, S.; Chen, T.; He, H.;
and root mean squared log errors (RMSLE) of all regions are less than 0.1, which demonstrate that the
Feng, Z.; Zhu, Y.; Liu, F.; Li, C. A
proposed method achieves high prediction accuracy with generalization capability and can accurately
Regional Industrial Economic
Forecasting Model Based on a Deep
predict the economic growth trends of different types of regions.
Convolutional Neural Network and
Big Data. Sustainability 2021, 13, Keywords: deep convolutional neural network; regional economy; industrial economic big data
12789. https://doi.org/10.3390/
su132212789
strengthen U.S. infrastructure and manufacturing, designed to promote circular and rapid
growth in the U.S. domestic economy.
However, due to current complex international political relations and dynamic macroe-
conomic development, methods to accurately predict the future development of regional
industrial economies based on current economic status and understand the interactions
between different industrial systems and their impact on regional economic development
have all become important issues in selecting regional pillar industries, enhancing the
competitiveness of cities and the synergistic development of regional economies. By mining
historical data on the economic development of each region in the country, this study in-
vestigates the key economic industries developed in each region and analyzes and predicts
the growth potential of regional industrial economic development to establish a theoretical
basis and value reference for the selection of advantageous industries in the region and the
coordinated development of each region.
At present, regional and industrial economy research is rich and wide-ranging and
involves relatively mature research methods. Scholars carrying out multilevel and multi-
perspective studies on different research topics using various research methods have
produced a steady stream of research results. In terms of research content, most of the
literature focuses on specific industries, such as agriculture [3], manufacturing [4,5], real
estate [6], logistics [7], finance [8], productive services [9], cultural industries [10], informa-
tion industries [11], and other economic industries. The association of regional industries
and their ripple effects [12] have also become the focus of research in recent years, and
several rationalized policy recommendations have been proposed to promote industrial
structure upgrading [13]. We introduced a multiregional economic correlation analysis
method based on big data [14]. These recommendations are powerful tools for judging the
economic contribution of industries and selecting leading regional industries. Tianren Yang
et al. investigated the residential mobility patterns of households living in low-income
neighborhoods and studied the neighborhood characteristics that influence their mobil-
ity [15]. Lingqiang Kong et al. summarized the big data-based urban environment, society,
and sustainability (UESS) research using a systematic review approach in combination with
bibliometric and thematic analyses [16]. Zaheer Allam and Zaynah A. Dhunny proposed a
new framework binding AI technology and cities while ensuring the integration of key
dimensions of culture, metabolism, and governance [17].
In terms of research methods, various economic forecasting models have been pro-
posed to predict the future development of relevant industries based on the current state of
the industrial economy. Tingting Zhang et al. proposed an FWA-SVR forecasting model to
forecast the tourism economy [18]. Cheng Mao-Lin et al. used the gray model GM (1,1)
to predict the growth trends of China’s real estate economy [19]. In another study [20],
China’s macroeconomic system was analyzed by constructing a BMF-VAR model to fore-
cast China’s macroeconomy. In [21], a deep-learning LSTM model used to forecast and
analyze the inflation rate in China achieved good performance. Ji Yao used commod-
ity price big data to construct a MIDAS model for analyzing and forecasting CPI and
PPI [19,22]. Xia Maosen used a deep neural network CNN-LSTM model to analyze and
forecast the Chinese consumer confidence index [23]. Pichayakone Rakpho et al. proposed
a Bayesian vector autoregressive (BVAR) model to forecast energy demand and supply
more accurately [24]. Yifei Lyu et al. constructed a dataset of 77 countries representing
over 90 percent of global GDP and forecasted US economic growth in downturns using
cross-country data [25]. Oscar Claveria et al. presented a machine-learning method for
sentiment indicators construction and economic forecasting with evolved confidence indi-
cators [26]. To enhance the prediction accuracy of COVID-19 and strengthen the economic
management and control, Xuan Tang et al. introduced a self-correcting intelligent pan-
demic prediction model [27]. To accurately forecast business failure prediction, Soo Young
Kim et al. developed three prediction models (entire period model, economic downturn
model, and economic expansion model) using WEKA3.9 [28].
Sustainability 2021, 13, x FO
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secondary, and tertiary industry growth values are combined to form panel data to identify
Sustainability 2021, 13, 12789 4 of 11
correlations between industries and the influence of industry data on later growth values
within n consecutive years using neural network convolution kernels. First, three CNN
layers (including convolution, batch normalization, ReLU activation function, and pooling
operations) are used to extract time series data features and potential industry connections.
The inputs of the fully connected layers are the output values of (1) and (2), and the
two fully connected layers integrate the outputs of the GDP and panel data networks to
further optimize the learning of the multichannel outputs of the two networks.
C kx ky
zl +1 (i, j) = ∑∑∑ Z l s x i + x, sy j + y wcl +1 ( x + y) + b
(1)
c =1 x =1 y =1
where zl (i, j) denotes the value of the i-th row and j-th column data of the feature image
(panel or sequence) in the l-th layer, C represents the number of channels, (k x , k y ) indicates
the size of the convolutional kernel, (s x , sy ) is the convolution step size, wcl +1 represents the
weight matrix of channel c of l + 1, and b is a bias value.
(2) Batch Normalization (BA): BA can stabilize the training process of the network,
improve training speed, and minimize problems (such as gradient disappearance and
overfitting [30] using the following expression).
where zl(b) (i, j) denotes the input value of the b-th sample (Row i, Column j) in the current
batch. Here, zl(b) (i, j) denotes the output value of the convolution operation; u(i, j) and
σ (i, j) denote the mean and standard deviation of the batch sample at that position (Row i,
Column j), respectively; and parameters γ and β are used to control the mean and variance
of yl(b) , respectively.
(3) Relu: Relu is an activation function used to recalculate and activate the data after
normalization as follows.
l x , x>0
y = x (3)
α ( e − 1) , x ≤ 0
where yl is the output value of the l-th layer after activation by the Relu function.
(4) Pooling: Down sampling, dimensionality reduction, and feature compression are
conducted on the output data of the convolutional operation. Maximum pooling is used in
the first and second convolutional layers, and the mean pooling operation is used in the
third convolutional layer.
(5) Loss function: The model uses the Huber loss function (as shown in Equation (4))
to optimize the prediction results as follows.
n
loss(y, y0 ) = n1 ∑ zi
i =1 2
0.5 y − _ _
− (4)
y , i f y i < 1
i _ i i
y
zi =
y − y − 0.5, otherwise
i i
_
where y and y denote the real and predicted values of the network, respectively.
(6) Optimization algorithm: For the loss function, the distance between the predicted
network output and true values is minimized by optimizing the network parameters.
The Adam optimization approach, which is solved iteratively by performing the gradi-
Sustainability 2021, 13, 12789 5 of 11
ent derivative of the loss function [31], is adopted for the optimization of the network
parameters. The Adam algorithm iterative formula is written as follows.
mt
θ t = θ t −1 − α √ (5)
vt + ξ
e t = β1 · m
m e t −1 + (1 − β 1 ) · g t
mt = me tt+1
1− β 1
vet = β 2 · vet−1 + (1 − β 2 ) · gt 2
vt = vett+1
1− β 2
where θt is the parameter value of the network at the t-th iteration, mt denotes the first
moment estimate, vt denotes the second moment estimate, and gt denotes the gradient
value at time t. α represents the learning rate during the iteration (0.01 in this paper); β 1
and β 2 both represent the exponential decay rate (β 1 is used to control the momentum of
the exponential shift and the weight of the gradient at generation t (0.9), respectively; and
β 2 is used to control the weight of the squared gradient at generation t − 1 (0.999)). ξ is a
very small positive number (1 × 10 − 8) designed to avoid the denominator of Equation (5)
being 0.
The transformed data follow the approximate normal distribution of Figure 3, which
basically satisfies the conditions of regression analysis.
In this paper, which is based on regional economic data in China from 1997 to 2020,
the regional data of five consecutive years are used as the network input, X, and the data of
the sixth year are used as the output, Y. Each region generates 19 sample data (each sample
is a matrix panel data with 5 rows and 5 columns), yielding a total of 19 × 31 = 589 sample
data. Due to the small number of samples, the network is easily overfitted with respect to
prediction learning for the 31 regions. To improve generalization capability and enhance
Sustainability 2021, 13, 12789 6 of 11
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the prediction performance of the network, the data with 589 samples are randomly fine-
tuned to generate new sample data, resulting in a total of 2565 sample data points. During
The
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for three industries.
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industries.
where yi and ŷi represent the true output value and network prediction of the ith
sample, respectively; and yi represents the mean value of the true value in the region.
(2) Root Mean Squared Log Error RMSLE
s
1 n
n i∑
RMSLE = (log(ŷi + 1) − log(yi + 1))2 (9)
=1
where yi and ŷi represent the true output value and network prediction of the ith
sample, respectively.
The value of R-squared is in the interval [0,1], and the closer its value is to 1, the more
accurate the prediction result is. In contrast, the traditional mean squared error (MSE)
value is influenced by the range of values of the original data, which is not sizeable enough
to determine the degree of accuracy. The value of RMSLE is not greatly influenced by the
size of the original data compared to the root mean square error RMSE; thus, it is easier to
discern the prediction accuracy with RMSLE.
In Table S1 (see Supplementary), the prediction accuracies of the three industries
in 31 provinces are summarized by the deep multilayer convolutional neural network
proposed in this paper. Figure 4 shows plots of the predicted and true values for Beijing,
Shanghai, Shaanxi, Xinjiang, Heilongjiang, Guizhou, Tibet, Guangdong, Shanxi, and Zhe-
jiang Provinces from 2002 to 2020. From Figure 4 and Table S1, it can be seen that the
Sustainability 2021, 13, 12789 8 of 11
deep convolutional neural network prediction model proposed in this paper can accurately
predict the economic growth values of the three industries in all regions. The R2 values
of the network model for the 31 regions are all greater than 0.9. With the exception of
R2 = 0.915671 for the secondary industry in the Shaanxi Province, R2 = 0.972776 for the
primary industry in Tibet, and R2 = 0.938831 for the primary industry in the Zhejiang
Province, the R2 values for the three industries are all greater than 0.98. In addition, only
the RMSLE of the secondary industry in the Shanxi Province is 0.114741 (which has a
relatively large error); the prediction errors of all the other regions are less than 0.1. This
indicates that the method in this paper achieves high prediction accuracy and can be used
to forecast the economic growth of regions with large variability.
The above experimental results and analysis indicate that the proposed forecasting
model has high-precision for predicting multi-industries and multiple economic indicators
of a region. The model can be easily used to the future economic development trends of
the regions. However, it has some limitations, as listed below.
(1) The multilayer CNN requires a large sample size to train the network, but the histori-
cal economic data of each region is general not sufficient due to the lack and missing
of historical data.
(2) The CNN has high prediction and recognition ability when it has seen similar sample
data; however, its prediction ability is deficient for the data that has never been
seen before.
(3) The predication model proposed in this work only considers the GDP; GDP grow
index; and the growth value of primary, secondary, and tertiary industry as inputs,
and it can also predict the future growth values of primary, secondary, and tertiary
industries in a region. However, regional economic forecasting should consider the
development trends of more precise industrial economies (e.g., financial industry,
tourism, ecological environment, population structure, etc.).
Future works:
Sustainability 2021, 13, x FOR PEER REVIEW 9 of 12
(1) Construction of a multiregional and multi-industrial structure economic index system.
The indicators of regional population structure, ecological environment, tourism,
logistics, service industry, agriculture and industry are integrated to build a more
(2) scientific and complete
Construction multiregional
of correlation andmodel
relationship multi-industry structure indicator
for the multiregional system.
and multi‐in‐
(2) Construction of
dustry structure. correlation relationship model for the multiregional and multi-
(3) industry structure.
Prediction model construction for multiregional and multi‐industrial structure eco‐
(3) Prediction model construction for multiregional and multi-industrial structure eco-
nomic indexes.
nomic indexes.
Figure 4. Cont.
Sustainability 2021, 13, 12789 9 of 11
Figure 4. Fitted curve of predicted and true values.
4. Conclusions
The accurate prediction of regional industrial economic development trends is crucial
for future economic decisions and sustainable coordinated development. To accurately
predict and analyze the economic development of three industries in each province, munici-
palities directly under the central government, and autonomous region of China, this paper
proposes a multilayer convolutional neural network prediction model that uses regional
historical economic data to train and learn a neural network. First, the historical data of
the 31 provinces are integrated, and the historical data of each of the three industries are
normalized and preprocessed. Due to large differences in the economic development levels
of each region, the data contain obvious bias characteristics that affected the prediction
accuracy of the neural network. For this reason, the data are first normalized to balance
the deviations among the indicators, and then the normalized data are transformed using
Equation (7) to balance the deviations among the regions. Second, by constructing panel
data and generating sample data, the sample size is increased to improve the generalization
Sustainability 2021, 13, 12789 10 of 11
capability of the network. The experimental results show that the proposed multilayer
convolutional neural network can accurately predict the economic development of each
region. One disadvantage of our method is that we only analyze and predict from the
combined data of GDP of primary, secondary, and tertiary industries, which cannot reflect
the development trends of each industry. Since the industries in the regions tend to influ-
ence each other and have complex correlations with each other, it would be more useful
to analyze and forecast from the perspective of industry big data. As future research, we
intend to collect the historical data of each industry in the regions and form more complete
regional economic big data by analyzing and integrating the data of each industry, i.e.,
population structure, education, agriculture, industry, service industry, finance, science
and technology, ecological environment, traffic conditions, water resource distribution, and
energy conditions, and then mine their correlations to construct a deep neural network
model. In addition, we will build a deep neural network model to forecast the development
of the all industries taken together.
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