Pattern Recognition by Neural Network Ensemble

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IT691 2009
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Pattern Recognition by Neural Network Ensemble
Joseph Cestra, Babu Johnson, Nikolaos Kartalis, Rasul Mehrab, Robb Zucker
Pace University
Abstract
This is an investigation of artificial neural network
ensembles and its application to predicting future trends
of the stock market. We discuss the importance of neural
networks and the theoretical benefit of using an ensemble.
The stock market is a complex and volatile system which,
if adequately forecasted, could yield profitable decisions
for traders. Conventional linear programming models
lack the structure for such a problem; however, neural
networks are particularly well-suited because of their
parallel and adaptive nature. Ensemble techniques
combined with the cross-validation training methodology
are used to produce the ideal results. The overwhelming
factors that influence the stock market as well as the
varying network structures, parameters, and algorithms
prove difficult to master, but the results are a step in the
right direction.
the unique ability to capture complex relationships
between inputs and outputs as well as learn from a set of
data without any underlying assumptions. We are
focusing our studies on the Multilayer Perceptron, which
is a feed-forward neural network trained under a
supervised learning algorithm. In order to train these
systems we feed a large set of inputs and desired
outcomes into the network upon which the network can
adapt. After many iterations of training, the ANN is able
to detect subtle patterns in large data sets and make
predictions based on what it has learned by past
observations. ANN’s have been successfully applied to
broad spectrum of applications including: financial
forecasting, targeted marketing, medical diagnosis, and
voice recognition. The ANN gives us a new way to
approach complex data intensive problems.
1. Introduction
1.1. Artificial Neural Networks
Even with the vast improvements in computing power,
there are many problems not suitable for a linear
programming paradigm. The conventional algorithmic
approach relies on a set of clearly defined instructions to
solve a problem. Unfortunately, this restricts the problem
solving capability of conventional computers to problems
that we already understand and know how to solve.
Problem solving is at the heart of computer science so it
is vital that we develop and analyze new computing
models suitable for real-world problems. One such model
is the Artificial Neural Network (ANN), which has the
remarkable ability to derive meaning from complicated or
imprecise data and can be used to extract patterns and
detect trends that are too complex to be noticed by either
humans or other computer techniques. The ANN derives
its design and inspiration from the human brain and
emulates biological neural networks in both structure and
function. Structurally, an ANN consists of a pool of
highly interconnected processing units called neurons
working in parallel to solve a problem. The main function
of an ANN is to learn from input and produce output
based on past observations. Artificial Neural Networks
are powerful because they are adaptive systems that have
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Figure 1. MLP Neural Network.
1.2. Ensembles
Ensemble techniques couple the output of a collection of
multiple neural networks to form one collective decision.
The critical issue in developing a neural network is
generalization: how well will the network make
predictions for cases that are not in the training set? In
reality, the network is trained to minimize the error on the
training set; however, this is not the same thing as
minimizing the error surface of the underlying and
unknown model. After all, the purpose is not to memorize
a pattern for a given data set but; more importantly, to be
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able to generalize knowledge learned to unseen data. A
network may be trained on a data set successfully, but
when that network is tested on some new data the
network may perform poorly. This means the network did
not generalize effectively. Poor generalization can be a
result of over-learning or over-fitting, which occurs when
a network is too complex for a problem or given quantity
of input. Over-learning most commonly occurs when the
number of input variables (and hence the number of
weights) is large with respect to the number of training
cases [5]. The use of neural network ensembles is
believed to improve generalization performance by
combating over-learning. Statistically, this occurs because
averaging predictions across models trained on different
data subsets, can reduce model variance without
increasing model bias [2]. Intuitively, dividing the input
variables among an ensemble enables the number of
training cases to remain constant while reducing the
network complexity. Ultimately, theory suggests that the
expected performance of an ensemble is greater than or
equal to the average performance of the members [2] [5].
1.3. Objective
The vision for this project is to construct an ensemble of
Multilayer Perceptron (MLP) Artificial Neural Networks
and to evaluate its performance on the task of financial
forecasting. Specifically, we hope to develop an ensemble
of networks that are each trained individually on different
sets of historical economic data, and then coupled to
create a collective decision regarding buying, selling, or
holding shares of stock. This kind of problem falls under
classification, where a given input needs to be classified
into a category. Since the decisions will be solely based
on the direction and change of the market index, the
network is ultimately predicting future trends of the stock
market.
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be a lucrative endeavor. Other reasons include: unlimited
access to historical data on the web and the existence of
almost limitless inputs that affect the stock market. The
stock market is an often erratic system that frequently
fluctuates in response to many factors. Most
programming paradigms are ill suited to make predictions
in such a complex and volatile system. Our objectives
include demonstrating that an ANN has the capacity to
learn the behavioral patterns of the stock market and to
show the benefits of using an ensemble of neural
networks for collective decision-making. We believe that
with a careful selection of inputs and design for our
ensemble we can develop a system that makes favorable
decisions in the stock market.
2. Design
We have chosen to use the Dow Jones Industrial Average
for our investigation and, if we succeed, similar efforts
will be applied to other stock indices in the future. Our
initial ensemble consists of six networks each trained on a
unique set of inputs. The output for each network will
represent a decision to buy, sell, or hold stock. The data
structure used for the output is an array of three binary
values, where each value corresponds to a decision. A
value of 1 is used to indicate a favorable decision and a
value of 0 will indicate an unfavorable decision. The
desired outcome for one trading day is based on the
direction (positive or negative) the stock market went that
day and the percentage of change. The threshold will be a
1% change in either direction meaning a market gain
equal or greater than that amount will yield a buy
decision, a loss equal or greater than that amount will
yield a sell decision, and any percentage change less than
that amount will yield a hold decision. A 1% change is
equivalent to a 100 point gain or loss from a previous day
closing of 10,000. Like many other factors this may be
adjusted for future purposes. The corresponding values
from each network output will be summed to create a
single output and the greatest value of the output set will
indicate the correct decision. For example, if two
networks produce outputs [.99, .75, .23] and [.88, .99,
.45] then the first item of the final output set would hold
the greatest value and the corresponding decision would
be made.
2.1. Training Data
Figure 2. One member of ensemble.
Financial forecasting is of particular interest because it is
a problem suitable for neural network ensembles and can
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The data used for training will consist of 21 input
variables, which represent factors that are believed to
affect the behavior of the stock market. The initial inputs
chosen include the following:
DJIA Close
DJIA Volume
Nikkei Index
Hang Sang Index
IT691 2009
Oil
Gold
10 year Treasury Note
FTSE Index
DAX Index
CAC 40 Index
Dollar-GBP
Dollar-Yen
Dollar-Euro
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NASDAQ
S&P 500
U.S Prime Rate
Federal Funds Rate
Inflation
Consumer Confidence
Unemployment
30 year Mortgage Rate
It is possible that some of the inputs will have greater
impact on the stock market than others so it is useful to
experiment with these values.
2.2. Data Division
Because we are using an ensemble of neural networks,
the input data was divided amongst each network. Each
network is fed with both a constant and unique input set.
The constants data used for all networks consists of
general economic data including bonds (10yr Treasury),
commodities (oil and gold), and DJIA stock data (close
and volume). The division of remaining training data was
done using basic knowledge about the economy rather
than by random. We coupled data that shared something
in common to create unique sets of input variables that we
believe would have a significant impact on the stock
market. The division is as follows:
Network 1 consists of three exchange rates: USD-GBP,
USD-Yen, and USD-Euro, which indicate the strength of
the U.S dollar.
Network 2 consists of U.S market data and includes the
closing values of two major indices; namely, NASDAQ
and the S&P 500.
Network 3 consists of Asian Market data and includes the
closing values of two major indices; namely, Hang Sang
and Nikkei.
Network 4 consists of European Market data and includes
the closing value of three major indices; namely, CAC 40,
DAX, and FTSE.
Network 5 consists of factors which affect money supply
and the purchasing power of that money. Inflation
directly correlates to the purchasing power of the U.S
Dollar while the Federal Funds and U.S Prime Rate both
strongly influence the supply of money.
Network 6 consists of data which indicates the welfare of
the general public. Unemployment has a direct impact on
the consumer and its effects permeate through the
economy. The Consumer Confidence Index indicates the
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attitude of the public toward the economy. The 30 year
Mortgage Rate indicates the ups and downs of the
housing market, whose conditions are critical to millions
of homeowners and families.
With limited knowledge in economics and the stock
market we hope we have coupled data in a way that has a
meaningful impact on our network output and at the very
least no negative effect.
2.3. Training Methodology
The purpose of training is to allow networks to acquire
knowledge or learn from a sample of data but the success
of training is dependent on how well that knowledge can
be applied to data outside the training sample; otherwise,
known as generalizing. In order to develop networks with
best possible performance we need a way to accurately
measure generalization error. Before we do that we need
an appropriate error function to calculate or characterize
the overall error of a network. We have chosen to use the
root mean squared function to approximate the total error
of a network for a given data set. Since our goal is to find
the network having the best performance on new data, the
simplest approach to the comparison of different
networks is to evaluate the error function using data
which is independent of that used for training. The most
common approach requires subdividing the sample data
into training, validating, and testing sets. Various
networks are trained with respect to a training data set.
The performance of the networks is then compared by
evaluating the error function using an independent
validation set, and the network having the smallest error
with respect to the validation set is selected. The
performance of the selected network should be confirmed
by measuring its performance on a third independent set
of data called a test set. The main criticism of this
methodology is that the size of the training set is
necessarily reduced. For this reason, we have opted to use
cross-validation. We will divide the data into n subsets
and train the net n times, each time leaving out one of the
subsets from training, but using only the omitted subset to
compute the error. This allows us to use all the data for
training; however, we will still need an independent set
for final testing.
3. Implementation
3.1. Programming
We adapted an existing neural network written in Python
for our investigation. Python is rich in text processing
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tools, it is easily extensible, and supports object-oriented,
procedural, and functional styles of programming. The
program code employs a back-propagation learning
algorithm and defines a network with one hidden layer.
3.2. Data Collection
7
9
10
12
14
.80
.81
.81
.81
1.1
27%
28%
45%
44%
28%
Table 2. Network 2 results.
The data was collected through the Web from sources
such as Yahoo Finance and stored in the CSV (Comma
Separated Value) format. CSV is the most common
import and export format for spreadsheets and databases.
Furthermore, Python has a built-in CSV module for
reading and writing. The data was collected for one year
and organized appropriately for the individual networks.
To facilitate the training process, data for each network
was organized in separate CSV files in order to limit the
need for parsing the data in python.
Hidden Neurons
5
7
9
10
12
14
Statistical Error
0.75
0.8
0.81
0.81
0.82
.85
Hit Rate
27%
33%
27%
31%
45%
27%
Table 3. Network 3, 4, 5, 6 results
3.3. Training, Validation, and Testing
Adjustments were made to the existing code to automate
training from the collected data and each neural network
was fed a unique subset of the training data. The initial
parameters for each neural network included: 5 hidden
neurons, 3 output neurons, and a .5 learning rate. We
experimented with these and other parameters and
compared network performance using the cross-validation
method. After choosing the parameters that yielded the
best generalization performance for each member of the
network we tested the ensemble using an independent set.
4. Evaluation
4.1. Cross-Validation
We performed the cross-validation method on each
individual network in order to select the proper amount of
hidden neurons to be used. We calculated both statistical
generalization error (using RMS) and the hit rate of the
network. The results are as follows:
Hidden Neurons
5
7
9
10
12
14
Statistical Error
.75
.80
.81
.81
.81
1.1
Hit Rate
30%
27%
28%
45%
44%
28%
Table 1. Network 1 results.
Hidden Neurons
5
4
Statistical Error
.75
Hit Rate
27%
The first two networks had the highest hit rate using 10
neurons, while the rest performed best with 12 neurons.
The generalization error remained fairly stable using up to
12 neurons. When 14 or more neurons were used, the
performance dropped off considerably for each network,
which means the network was probably over-learning.
We ultimately chose to use 12 neurons for each of the
networks in the ensemble.
4.2. Ensemble Results
As described in the project design, we trained each
network individually. Using a random sample of the data
we tested each network and the individual output sets
were combined. The ensemble output set was then
compared to the desired results so that we could calculate
both statistical generalization error as well as the hit rate.
5. Conclusion
The results were not as accurate as we would have hoped;
however, we believe it is a step in the right direction. The
complexity of the problem and of network design has
served as a reminder that financial forecasting is no easy
task. As far as the stock market goes, there are numerous
factors that may or may not affect future trends. Selecting
the ideal input variables can be a daunting task and
coupling that data for the use of ensemble only adds to
the complexity. It would be helpful to combine ANN
research with studies that show the affects of economic
data and other factors on the stock market. As far as
ANN’s go, there are numerous factors to contemplate:
number of hidden layers, number of neurons, size of
training sample, error function, training methodology,
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learning algorithm, network structure, data representation,
ect. The wrong choices lead to poor performance. A small
network may not learn what you want it to learn, while a
large network will learn slowly, may get stuck on local
maxima, and may exhibit over-fitting. Overall,
developing ANN’s is as much an art as a science. One
must carefully select data samples and network design
parameters and undergo thorough experimentation, in
order to get the best results. The successful deployment of
ANN technology requires time and experience. ANN
experts are artists; they are not mere handbook users [4].
6. References
[1] Mertz, David, and Andrew Blais. “An introduction to
neural networks: Pattern learning with the backpropagation algorithm”. 1 July 2001. Available:
https://www.ibm.com/developerworks/library/l-neural/.
[2] (Electronic Version): StatSoft, Inc. (2010). Electronic
Statistics Textbook. Tulsa, OK: StatSoft. Available:
http://www.statsoft.com/textbook/stneunet.html.
[3] Galkin, Ivan. “Crash Introduction to Artificial Neural
Networks”. Available: http://ulcar.uml.edu/~iag/CS/Introto-ANN.html.
[4] Fahey, Colin. “Neural network with learning by
backward
error
propagation”.
Available:
http://www.colinfahey.com/neural_network_with_back_p
ropagation_learning/neural_network_with_back_propagat
ion_learning_en.html.
[5] Warren S. Sarle. AI FAQ/neural Nets. Available:
http://www.faqs.org/faqs/ai-faq/neural-nets/.
[6] http://finance.yahoo.com/.
[7] http://www.oanda.com/currency/historical-rates.
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