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ICCWAMTIP.2016.8079870

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RESEARCH ON FINANCIAL TIME SERIES FORECASTING BASED ON SVM
YANG YUJUN1,2,3, YANG YIMEI2, *, LI JIANPING1
1
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
School of Computer Science and Engineering, Huaihua University, Huaihua, 418008, China
3
Hunan Provincial Key Laboratory of Ecological Agriculture Intelligent Control Technology, Huaihua, 418008, China
Email: yym@hhtc.edu.cn
the financial time series. The experimental results show the
prediction accuracy of this approach based on the support
vector machine.
Abstract:
The support vector machine (SVM) is a machine learning
method developed based on statistical learning theory. The
SVM is widely used in classification and prediction. Since the
financial time series is complex, the traditional forecasting
methods are less reliable. In this paper, we research on
financial time series forecasting based on the support vector
machine. Although the speed of prediction process is slow, it
can improve the prediction accuracy of the financial time
series. The experimental results show the prediction accuracy
of this approach based on the support vector machine.
2.
Support vector machine (SVM) was developed by
Vapnik and his colleagues. SVM emerged from research in
statistical learning theory on how to regulate generalization
in learning, and to trade off structural complexity against
empirical risk. Support vector machine for nonlinear
separable case, we can use a nonlinear function M (x) to
map data into a high dimensional feature space, then to
create optimized hyper-plane in feature space. The equation
of this super plane can be expressed as follows:
˄1˅
WT x b 0
Sigmoid function is used to map the independent
variables to (0,1), the value of the map is considered to
belong to the probability 1.
Assuming that there is a function
Keywords:
SVM; Forecasting; Time series; Support vector machine;
1.
Introduction
In many practical applications, SVM has displayed its
lots of outstanding ability, especially in prediction and
classification problems. In real-world applications, many of
the problems are complex, inherently noisy, non-stationary
and deterministically chaotic [1]. To reduce the complexity
space and time of the support vector machine, many
improved algorithm has been applied successfully. One
method is to obtain low-order approximation of the nuclear
matrix by greedy algorithm [2], or sample [3], or
decomposition matrix. Another method is to improve the
efficiency of SVM algorithm block. A third approach is to
avoid quadratic programming problems, such as central
support vector machine algorithm [4], the scale [5] method.
The financial time series are complex and inherently noisy
time series. The noisy characteristic refers to the
unavailability of complete information from the past
behavior of financial markets to fully capture the
dependency between future and past prices. Therefore,
stock market prediction is regarded as one of the most
challenging tasks of time series forecasting. In this paper,
we study the financial time series forecasting based on the
support vector machine. Although the speed of prediction
process is slow, it can improve the prediction accuracy of
978-1-5090-6126-6/16/$31.00 2016 IEEE
SVM method theory
hT ( x)
g (T T x)
1
1 e-T
T
x
(2)
Where x is the n-dimensional feature vector, the
function g is logistic function. The probability of the
function belongs to 1.
P(y 1| x;T )
P( y
hT ( x)
0 | x;T ) 1 hT ( x)
(3)
Thus, when we judge a new feature which belong to
the class, we only need to calculate the value of hθ(x). If it
is greater than 0.5 it belongs to the y=1 class. In the
meantime, if it is less than 0.5 it belongs to the y=0 class
correspondingly.
3.
SVM Model
Support vector machine is based on a linear division.
346
But you can imagine, not all data can be linear division.
Here is a simple example, assuming that there is a
two-dimensional plane, there are two different types of data
on the plane respectively. Because these data are linearly
separable, so we can use a line to separate the two kinds of
data. This line is equivalent to a hyperplane, the
corresponding hyperplane side of the data points of the Y is
-1 and the other side of the Y is 1.
Because the above sample is linearly separable, the
original spaced requirements can be met. In the determined
super plane w*x+b=0, |w*x+b| can express the distance
from point x to hyperplane. By observing whether the
symbol of the w*x+b and the class marked y can be judged
correctly, we can determine whether the classification is
correct or not and can determine the correctness of the
classification using (y* (w*x+b)). So we have the concept
of function interval (margin functional). Define function
interval as following.
J y(ZT x b)
4.
yf ( x)
Fig.1 the daily opening price of HIS
˄4˅
Experimental data and empirical results analysis
To indicate the prediction accuracy of this approach
based on the support vector machine, we choose two
indices from real stock market as experimental data. The
two indices are the SP500 and HSI. Here the SP500 is short
for the Standard & Poor's 500 which is an American stock
market index based on the market capitalizations of 500
large companies stock. The HSI is short for the Hang Seng
Index in Hong Kong of China. The original sample data
which cover 3762 days from the period July 9, 2001 to
December 24, 2015 are obtained from Finance of Yahoo
free of charge. The data sets of every index include five
daily properties: opening price, highest price, lowest price,
closing price and transaction volume. Fig.1 and Fig.2 show
the daily opening price of HSI and SP500 separately.
Firstly, we select the first to 3761st the daily opening
price of trading days as the independent variable of SVM
function. Correspondingly, we select the second to 3762nd
the daily opening price of trading days as the dependent
variable of SVM function.
In order to facilitate the calculation, we implement the
normalization of all data and map all the data to the range
between 1 and 2.
Fig.2 the daily opening price of SP500
Fig.3 the contour of parameter selection result of SVR
347
In order to achieve the best prediction results, we first
look for a better regression parameters c and g. Fig.3 and
Fig.4 show the parameter selection result of SVR. We use
the regression analysis to get the best parameters for the
SVM network training.
Fig.6 the result of final regression prediction error
Fig.4 the 3D view of parameter selection result of SVR
We use the best parameters of c and g to train the SVM
and perform regression prediction on daily opening price of
the HSI and SP500. The final regression prediction, error
and relative error results of SP500 show in Fig.5, Fig.6 and
Fig.7 separately. To display more clearly the prediction
result of the Fig.5, we enlarge the part range from 2780 to
2900 of the Fig.5 in Fig.8.
Fig.7 the result of final regression prediction relative error
Fig.5 the result of final regression prediction
Fig.8 the part result of final regression prediction
348
5.
Conclusions
Since the financial time series is complex, the
traditional forecasting methods are less reliable. In this
paper, we study on financial time series forecasting based
on the support vector machine. In this experiment, we
choose two indices of SP500 and HSI from real stock
market as experimental data. The experimental results show
the prediction accuracy of this approach based on the
support vector machine. We analysis the result of final
regression prediction and find that the approach has better
effect in the mature market than in the immature market.
Acknowledgements
This work is supported by the National Natural
Science Foundation of China (No.61370073), Sichuan
Province Science and technology support program
(No.2013GZX0165), the Constructing Program of the Key
Discipline in Huaihua University, the Scientific Research
Fund of Hunan Provincial Education (No.14C0886), the
Science and Technology Plan Projects of Huaihua City, the
Key Laboratory of Intelligent Control Technology for
Wuling-Mountain Ecological Agriculture in Hunan
Province (No.ZNKZ2014-9).
Fig.9 the result of final regression prediction error for HSI
References
[1] Bin Gui, Xianghe Wei, “Financial Time Series
Forecasting Using Support Vector Machine”, 2014
Tenth International Conference on Computational
Intelligence and Security, pp. 39-44, 2014.
[2] Mingjun Song, Rajasekaran, S., “A greedy algorithm
for gene selection based on SVM and correlation”,
International Journal of Bioinformatics Research and
Applications , Vol 6, No. 3, pp. 296-307, 2010.
[3] Huichuan Duan, Naiwen Liu, “A Greedy Search
Algorithm for Resolving the Lowermost C Threshold
in SVM Classification”, 2013 Ninth International
Conference on Computational Intelligence and
Security, Leshan, China, pp. 190-193, Dec. 2013.
[4] Du, Jing-Yi. Hou, Yuan-Bin, “Greedy algorithms with
Kernel matrix approximation”, Moshi Shibie yu
Rengong Zhineng/Pattern Recognition and Artificial
Intelligence, Vol 20, No. 1, pp. 138-143, Feb. 2007.
[5] Shen Zhaoqing, Peng Yuhua, and Shu Ning, “A Road
Damage Identification Method based on Scale-span
Image and SVM”, Geomatics and Information Science
of Wuhan University, Vol 38, No. 8, pp. 993-997, Aug.
2013.
Fig.10 the result of relative error for HSI prediction price
In order to compare the effect of the approach based
on the support vector machine applied to different data, we
give the result of final regression prediction relative error in
Fig.10 and the result of final regression prediction error in
Fig.9. Observing the Fig.10 and Fig.7, we find that the
relative error of Fig.7 is less than the relative error of Fig.
10. Here the Fig.10 and Fig.7 show the relative error of
prediction price for HSI and SP500 separately. It is
generally known that HSI is a stock market index of China
and SP500 is an American stock market index. Since
American stock market is more mature than Chinese stock
market, the results of Fig.10 and Fig.7 show that the
approach has better effect in the mature market than in the
immature market.
349
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