This paper reviews and classifies neural network variants used for stock market prediction, highlighting their enhanced features and advantages over standalone neural networks. It presents comparative analyses of different models, input variables, performance measurements, and the importance of data preprocessing in improving prediction accuracy. The findings emphasize that these neural network variants significantly improve forecasting ability, making them preferable for analyzing and evaluating market behavior.

1. Tutorial Paper
Proc. of Int. Conf. on Control, Communication and Power Engineering 2013
A Comparison of Stock Trend Prediction Using
Accuracy Driven Neural Network Variants
M.P. Rajakumar1 and Dr. V. Shanthi2
1 Research Scholar, Sathyabama University, Chennai-600119, Tamil Nadu, India.
Associate professor, St. Joseph’s College of Engineering, Chennai-600119, Tamil Nadu, India
Email: rajakumar_mp@yahoo.com
2 Professor, St. Joseph’s College of Engineering, Chennai-600119, Tamil Nadu, India
Email: drvshanthi@yahoo.co.in
frequency, higher order linear data, fast convergence, need
only few data, providing more parsimonious interpolation in
high dimension spaces when modeling sample are sparse.
These additional advantages of neural network variants
provide fast forecasting of stock market prices with high
quality prediction accuracy compared to standalone neural
network.
This work focuses on the applications of available neural
networks variants to predict stock market indexes. A stock
index is a method of measuring the value of a section of the
stock market. It is computed from the prices of selected stocks
in the forecasting process and the main firm characteristics
are not taken into consideration. Authors developed models
to overcome this limitation. Neural network variants may be
applied to diverse markets to forecast the stock market
indexes.
The purpose of this work is to review and classify the
neural network variants to stock market prediction. Results
are presented in three tables. The first table lists the neural
network variants, descriptions, enhanced features and scope
for future work. The second table presents the stock markets
modeled by different authors, independent variables to the
stock market, performance measurement & data preprocessing
techniques used. The final table summarizes some useful
modeling information like sample size of the work, validation
set, training process, network structure and the membership
function used.
The classification of neural network variants techniques
used in analyzing and evaluating stock market is presented.
The major applications of this technique include obtaining
supplementary information related to market behavior,
relationship among factor influencing performance, input data
compassion among other things.
Abstract—In the recent scenario, nevertheless to say, modern
finance is facing many hurdles to find effective ways to gather
information about stock market data at one shot. At the same
time it is inevitable for both individuals & institutions to
visualize, summarize & enhance their knowledge about the
market behavior for making wise decisions. This paper surveys
recent literature in the domain of neural network variants to
forecast the stock market trends. Classification is made in
terms of dependant variables, data preprocessing techniques
used, network structure, performance analysis and other
useful modeling information. Through the surveyed papers it
is shown that the neural network variants are widely accepted
to study and evaluate stock market behavior compared to
standalone neural network.
Index Terms—neural network variants, standalone neural
network, classification, stock market forecasting.
I. INTRODUCTION
A stock market is a primarily a virtual exchange of
securities that is, shares and debentures, which companies
use as a means of raising finance and derivatives. Forecasting
the stock trend is highly challenging since the stock market
data are highly time variant data and non linear pattern. To
introspect challenges in stock market we need to overcome
the impediments and strive for further improving our focus
on prediction of share market.
Neural networks play an important role in predicting the
stock market prices accurately. Numerous researches on the
application of neural network in forecasting problem have
proven their advantages over statistical methods. The neural
network has the advantages like analyzing complex pattern,
process qualitative data, no restrictive assumption, overcome
autocorrelation and can handle noisy data. But it has some
limitations like need of high quality data, variables must be
carefully selected a priori, knowledge of its internal working
is never known, risk of over fitting, required definition of
architecture, long processing time, possibilities of illogical
network behavior, large training sample, need lot of
computational resources and limited to specific problems
when applied. The above limitation of neural network can
be overcome neural network variants in predicting the stock
market behavior.
The variants of neural network provides enhanced
features such as rapid training, reducing extra effort on
scrutinizing training data, shorter computational time, higher
© 2013 ACEEE
DOI: 03.LSCS.2013.2.531
II. COMPARISON OF NEURAL NETWORK TYPES
Table I presents description of neural network variants
for stock market forecasting, comparative performance, enhanced features and the scope for future work. The description provides information on the author’s proposed work,
objectives and methodology. The comparisons made by different authors against different model are listed. These networks showed comparable results. The enhanced features of
neural network variants include high speed learning for large
volume of data, less computation time, fast convergence,
73

2. Tutorial Paper
Proc. of Int. Conf. on Control, Communication and Power Engineering 2013
combining the strengths of different fields like fuzzy system,
genetic algorithm, wavelet theory, spatial and temporal information and regarded as open box rather than black box. The
possible extension to enhance the different types of neural
networks for predicting the share price is presented in future
work section.
Table II lists stock market, input variables, performance
measurement and data preprocessing techniques used. The
stock markets modeled by different authors are listed. The
number of independent variables used in each work differs.
Some authors used 40 input variables .The most commonly
used inputs are stock market daily low price, high price,
maximum price and minimum price. Most authors predict the
stock market price and buy/sell behavior of the stock as
output. The performance measures can be classified as
statistical & non-statistical measures. Statistical measures
include AMAPE, ARV, CC, FPE, MAD, MAPE, MSE, NMSE,
R2 and POCID. Non- statistical performance includes hit
rate(time-first),hit rate(space-first),rate of return, annualized
return, cumulative return, annualized volatility, information
ratio, maximum drawdown, liquidity cash, portfolio value,
number of computational steps and speed up ratio. In most
researches the input data consists large amount of historical
data. This reduces the effectiveness of the training. This
may be overcome by preprocessing the data such as data
normalization, scaling of data between ranges of 0 and 1,
reduction in dimensionality, principal component analysis,
EDA optimization and some statistical procedures.
Table III lists sample size, validation set, training process,
network structure and its membership function. The proper
sample size enhances the accuracy of stock market prediction.
To predict established stock index large amount of data is
required due to changes of stock market in long run. Sample
size chosen by author is daily stock data with few cases of
missing data. It ranges from 2 years of stock data to 11 years
of stock data. Most authors divide the input series into
training set and validation set. But the percentage of the
sample size used for validating the result of the model varies
from 20 % to 50%. The training process used for variant
neural network types are EBP and Gradient descent learning
algorithms. Some authors proposed new training algorithm
like supplementary training process which has the features
of high speed learning with a large volume of data rate and
less calculation load. The average number of hidden layer
used in the network is one or two. Only FFNN uses 5 hidden
layers which measures the complexity of training algorithm.
The most common membership functions employed are
sigmoid function and Gaussian function.
Table I. COMPARISON OF NEURAL NETWORK VARIANTS
NN
variants
GFNN [9]
Descriptions
An intelligent decision support
system which measures all the
qualitative events in addition to
quantitative factors that may
influence the stock market were
developed. This novel method
consists of 3 parts namely factors
identification, qualitative model
and decision integration. The
fuzzy Delphi method was
employed to capture the stock
expert’s
knowledge
and
transformed to the acceptable
format of GFNN.
Comparative performance
analysis
The ANN which considers only
quantitative
factors
is
outperformed by the proposed
system in the learning accuracy,
buy-sell clarity and buy-sell
performance
Enhanced features
Future work
Capture the stock expert’s
knowledge, decrease the
training time and avoids
local minima
Real-number
coding
approach can be applied
in addition to binary
coding approach. It can
replace FFNN with EBP
learning algorithm
PbNN [3]
A set of trading strategies to
translate the forecasts into
monetary returns using PbNN
was developped. The significant
impact of the investment horizon
was also investigated. PNN was
built on the Bayesian classifier
which was capable of classifying
a sample with the maximum
probability of success
Comparison
is
based
on
performance statistics & trading
profits. PbNN has stronger
predictive power than both GMM
& RW
Training is rapid, reduces
extra effort on scrutinizing
training data, provides the
Bayesian probability of
the class affiliation to
make periodic decision on
asset allocation, inherently
parallel structure
Include a set of adaptive
thresholds
which
changes dynamically in
accordance with some
opportunity cost
MNN [16]
An attempt was made to model
and predict buying and selling
timing prediction system for
stocks and analysis of internal
representation.
MNN
was
characterized by a series of
independent NN moderated by
some intermediary.
The MNN produces a much
higher Correlation Coefficient
than MRA
High speed learning with
a large volume of data,
eliminate the need for
changing
parameters
depending on the amount
of learning data, reduce
the calculation load
Adaptation of network
model
that
has
regressive
connection
and
self-looping,
a
system in combination
with statistical method
must be developed
© 2013 ACEEE
DOI: 03.LSCS.2013.2.531
74

3. Tutorial Paper
Proc. of Int. Conf. on Control, Communication and Power Engineering 2013
HTLNN [5]
CBDWNN
[10]
HONN [2]
GRNN [13]
AWNN [8]
HSTDNN
[4]
WNN [1]
PNN [6]
The HTLNN which integrated the
supervised
MLP
with
unsupervised Kohonen network
for predicting the chaotic stock
series was presented. HTLNN
which contains short term
memory at the input processing
unit to provide the tap delay filter
to hybrid network
An integrated system CBDWNN
by combining dynamic time
windows, CBR & ANN for stock
trading prediction was developed.
This hybrid system consisted of
screening out the outstanding
stocks and use BPN to predict the
wave peak and wave trough of
stock price
A method of forecasting which
was capable of identifying and
dealing with discontinuities nonlinearties and high frequency
multi-polynomial
components
were proposed. HONN reduced
the need to establish the
relationship between inputs when
training
The future stock price companies
acting in Tehran stock exchange
used the most effective variables
related to the stock with the help
of GRNN was estimated.This
novel model could approximate
any arbitary function from
historical data.
one-period
continuously
compounded return series as
AWNN model for the day ahead
electricity market clearing price
forecast was presented. AWNN
introduced wavelets as activation
function of hidden neurons in
traditional FFNN with a linear
output neuron
A new approach which performs
testing process in the frequency
domain instead of the time
domain was proposed for fast
forecasting of stock prices. The
operation of HSTDNN relied on
performing cross correlation in
the frequency domain between
the input data and input weights
of the NN
Forecasting stock prices in
Nigeria stock market industry
using WNN was proposed.
Learning of WNN consisted of
changing the contents of the lookup table entries
A spatio temporal model for stock
price prediction was performed.
Both spatial and temporal
information
synchronously
without slide time window was
presented. Multidimensional time
series was also modeled. The two
models of PNN could handle
various
time-space
series
problems especially for large
scale of data
© 2013 ACEEE
DOI: 03.LSCS.2013.2.531
The HTLNN outperforms the
TLFN and HGUTLN in the
quality and accuracy of the
prediction
Combine the strengths of
both
supervised
and
unsupervised networks
HTLNN
is
being
investigated
for
integration with other AI
techniques
such
as
GA,FS
CBDWNN makes good trading
decision compared to CBR, BPN
even when the trend is downward
Combine the strengths of
dynamic windows, CBR
& ANN.
Time
window
CBDWNN
can
dynamically
GP model does well and
outperforms HONN, RNN, MLP,
ARMA, MACD and NAIVE
trading strategy
Fast learning network
with increased learning
capabilities,
shorter
computation
time.,
regarded as open box
rather than black box, able
to
simulate
higher
frequency, higher order
non-linear data
Internal structure is not
problem
dependent,
suitable for scattered data,
network model can be
taught immediately, solve
any
problems
in
monotonous function
Leverage factors can be
set
dynamically
in
reality. Leverage costs
also apply non trading
days
The proposed hybrid model
outperforms the literatures and
considering the extreme volatility
of spike signal, the price spike
forecast accuracy level of the
proposed system is good as
compared with literature
Universal
&
L2
approximation properties,
Consistent
function
estimator
Using
modern
SCADA/EMS systems
we can evaluate the
system
status
and
perform very short term
price forecasts which
improve the forecasting
of spikes
The
author
proved
mathematically and practically
that the number of computational
steps required for the HSTDNN is
less than TTDNN
Number of computation
step is less, less memory
capacity
The complexity of the
cross correlation in the
frequency domain can
be reduced
When compared with SES model,
the WNN forecasting tool proved
to be more accurate
Training takes only one
epoch , highly flexible,
fast learning algorithms
The other statistical
forecasting tools in
comparison with WNN
may be used
Time first & Space-first structures
of PNN performs higher hit rate
than BNN, HMM and SVM in
prediction of daily stock price
Combine the spatial and
temporal
information
together, simulate the
biological
neuron
physiologically
better,
decrease the time for
aggregating information
from
different
time
segments
The
generalization
ability of PNN for new
coming data may be
addressed in future
GRNN method is better than
linear regression method in
estimation & more descriptive
75
of
set
The number of input
variables
may
be
reduced
for
better
accuracy

4. Tutorial Paper
Proc. of Int. Conf. on Control, Communication and Power Engineering 2013
MMNN
[14]
TIMTAEF
method,
which
performs an evolutionary search
for the minimum dimension in
determining the characteristic
phase space that generates the
financial
time
series
was
presented. This novel model
searched for the particular time
lag capable of a fine tuned
charactererization of the time
series and estimates the initial
parameters of the MMNN.
The prediction of the proposed
model obtained a performance
much better in terms of evaluation
function than the TAEF &
MRLTAEF model
Overcome the random
walk dilemma for stock
market prediction
GMDHNN
[15]
A GMDH type Neural Network
and genetic algorithm was
developed for stock prediction of
cement sector in Tehran stock
exchange. A model could be
represented as a set of neurons in
which different pairs of them in
each layer were connected
through a quadratic polynomial
and therefore produce new
neurons in the next layer
GMDHNN
outperforms
traditional time series method and
regression based models in
prediction accuracy
Best optimal simplified
model for inaccurate,
noisy or small data sets,
simple structure than
traditional neural network
models, higher accuracy
FLFNN [7]
The proposed hybrid FLFNN
used non linear combination of
input variables to predict the
future stock close price. FLFNN
is a flat net without a hidden
layer. The hyperplanes generated
by the FLFNN produced greater
discrimination capability in the
input pattern
The proposed hybrid model
forecasts the stock close price
accurately with minimum error
rate than FLANN
Large
reduction
in
computation requirement,
learning algorithm is
simple, provides greater
discrimination capability
in the input pattern space,
fast convergence
Fuzzy sets can be filled
with suitable relations
that will be capable of
detecting
various
attributes
of
stock
market
FLANN
[11]
A simple FLANN network was
proposed whose randomly chosen
weights were optimized with BP
and DE algorithm respectively to
predict the stock price indices one
day, one week, two weeks and
one month in advance. It had the
capability to form complex
decision regions by creating non
linear decision boundaries
To increase prediction accuracy
and reduce search space and time
for achieving the optimal
solution, the combination of
WNN with fuzzy knowledge was
used. FWNN which integrated
wavelet functions with the TSK
fuzzy model. The proposed
network was constructed on the
base of a set of TSK fuzzy rules
that included a wavelet function
in the consequent part of each
rule
The seemingly chaotic behavior
of stock markets can be
represented using EDA based
LLWNN technique.Local linear
models should provide a more
parsimonious interpolation in
high-dimension spaces when
modeling samples are sparse.
The DE optimized FLANN a
proving it’s superiority compared
to BP optimized FLANN
Efficient global optimizer
in the continuous search
domain,
fast
convergence,
requires
only few parameters
The
performance
comparison
can
be
extended
with
DE
optimized PSO
Result, demonstrates that FWNN
with DE has better performance
than FWNN with BP, FFNN and
ANFIS
Combine the strengths of
wavelet theory, fuzzy
logic and neural networks,
fast training speed, ability
to analyze non-stationary
signals to discover their
local details, self learning
characteristic
that
increases the accuracy of
the prediction
The complexity of the
network
can
be
minimized
LLWNN outperform
prediction accuracy
Learning
efficiency,
structure
efficiency,
provides
more
parsimonious
interpolation
in
high
dimension spaces when
modeling samples are
sparse
It can be extended to
long term trends also
FWNN
[12]
LLWNN
[17]
focused on input data, data preprocessing methods, network
structure, membership function used, comparative studies,
performance measures and other useful modeling information.
The observation is that the neural network variants are
CONCLUSIONS
This study has surveyed articles of neural network
variants to predict stock market values. This study has
© 2013 ACEEE
DOI: 03.LSCS.2013.2.531
WNN in
Future
works
can
consider
the
development of further
studies, in terms of risk
and financial return, in
order to determine the
additional economical
benefits.
Also
investigate
the
performance
of
proposed method with
other financial time
series with components
seasonality,
impulses
steps
More price indices that
effect on stock price can
be used for accurate
stock price prediction,
proposed model can be
applied for the firms in
other sectors
76

5. Tutorial Paper
Proc. of Int. Conf. on Control, Communication and Power Engineering 2013
Table II. LIST
NN
variants
GFNN
Stock market
OF
SURVEYED STOCK MARKETS, I NPUT, OUTPUT & DATA PREPROCESSIN
Input variables
Performance measures
Data preprocessing
Taiwan
exchange
stock
Quantitative & Qualitative input
variables
MSE
Normalized to [0, 1]
PbNN
Taiwan
exchange
stock
FPE
Statistical testing procedure
MNN
Tokyo Stock exchange
CC
HTLNN
Kuala Lumpur stock
exchange
CBDWNN
Taiwan stock market
HONN
Greek stock market
TS,TB,DS3,DS6,DS12,GC3,GC6,G
C12,PC3,PC6,PC12,GNP3,GNP6,G
NP12,GDP3,GDP6,GDP12,CPI3,CP
I6,CPI12,IP3,IP6,IP12
Technical and economical indexes(
curve, turn over, interest rate, foreign
exchange rate, New-York DowJones average and historical share
prices)
Inter day stock data that is intraday
high, intraday low, close prices &
volume of the stock ticker
Highest stock price, lowest stock
price
Closing stock price
Teaching data are often irregular.
Such data is preprocessed by log
or error functions to make them
regular. It is then
normalized
into [0, 1] section.
All the data in the input series is
normalized to a value between 0
&1
Normalized to a value between
zero and one
Confirmation filters
GRNN
Tehran (Iran)
exchange
PJM market
AWNN
stock
HSTDNN
financial
variables
&
macroeconomic variables
Historic price data upto day (D-1) &
explanatory data upto day (D-1)
stock market price
Liquidity
value
cash,
Portfolio
RR
Annualized
Return,
Cumulative
Return,
Annualized Volatility,
Information
Ratio,
Maximum Drawdown
R2, MAPE, MSE, AMAPE
AMAPE, Variance, MSE
Nigeria
stock
exchange
Yahoo finance stock
market
Daily stock closing prices.
Hit-rate (Time-First), Hitratio (Space-First)
Stock market price
MSE, MAPE, NMSE or
THEIL, POCID, ARV,
Fitness Function
GMDHNN
Alliance
Financial
Corporation
,
BancFirst Corporation
,First
Citizens
Bancshares
Inc
,
Westamericaa Bancorp
Iranian stock market
EPS, PEPS, DPS, PE, E/P
R2, RMSE, MAD
FLFNN
SENSEX & NSE
RMSE, MAPE
FLANN
NSE, BSE and INFY
Non-linear combination of input
variables
The Indian stock prices with few
technical indicators like SMA, EMA
Stock price (low, high, open, close)
Opening price, closing price and
maximum price
CC, MAP, MAPE
Reduction in dimensionality
Number of Computation
Steps, Speed up ratio
MSE
Open price, highest price, lowest
price, closing price & stock volume.
Independent component analysis
WNN
PNN
MMNN
FWNN
LLWNN
NASDAQ
stock
market and
S& P
CNX NIFTY stock
index
suitable for stock market forecasting. Analysis demonstrates
that neural network variants outperform standalone neural
network and conventional models. They return better results
and higher prediction accuracy. However, difficulties arise
when defining the generalization structure of the network
like number of hidden layer, number of hidden neurons, etc.
AWNN
CBDWNN
FLANN
GMDHNN
GRNN
HONN
HSTDNN
HTLNN
LLWNN
MMNN
Adaptive wavelet neural network
Case based dynamic window neural
network
Functional link artificial neural network
© 2013 ACEEE
DOI: 03.LSCS.2013.2.531
RMSE
FWNN
GFNN
APPENDIX A NEURAL NETWORK VARIANTS
77
Data preprocessed to the range
[0,1]
All the inputs are normalized
within a range of [0, 1]
All the input and output data are
scaled in the interval [0, 1]
MAPE, RMSE
FLFNN
All time series were normalized to
lie within the range [0,1]
Input parameters are optimized by
EDA
Functional link fuzzy logic neural
network
Fuzzy wavelet neural network
Genetic algorithm based fuzzy neural
network
Group method of data handling
neural network
General regression neural network
Hybrid higher order neural network
High speed time delay neural network
Hybrid time lagged neural network
Local linear wavelet neural network
Hybrid model composed of modular

6. Tutorial Paper
Proc. of Int. Conf. on Control, Communication and Power Engineering 2013
Table III. SUMMARY OF USEFUL MODELING INFORMATION
NN variants
Sample size
Validation set
Training process
Network structure
GFNN
7 years of stock data
(1991-1997)
Training sample (19941995) & testing sample (Jan
1996-Apr 1997)
EBP
Input layer, one or more
hidden layer & output
layer
PbNN
11 years of stock
data (Jan 1982 - Aug
1992)
Rolling
approach
MNN
Weekly
learning
data from Jan 1985Sep 1989
10 years of stock
information
Sample estimation period
(Jan 1982-Aug 1987)
out of sample period (Sep
1987-Aug 1992)
Learning data (2/3) &
teaching data (1/3)
Learning data (50%)
testing data (50%)
HTLNN
CBDWNN
HONN
2 years of stock data
(Jan 2004 – Dec
2005)
8 years of stock
data(Jan 2001- Dec
2008)
GRNN
2 years of stock
data (Jan 2005-Dec
2006)
Training data:29-01-2001 to
03-05-2006
Testing data :04-05-2006 to
30-08-2007
Out-of-sample data: 31-082007 to 31-12-2008
6 years of macro &
financial variables
(100 companies)
AWNN
Training data & testing data
Training set, validation set
& generalization set
Supplementary
learning
Input layer,
hidden
layer & output layer
Standard
function
sigmoid
Sliding window with
the size of twenty
points
BPN & supervised
learning
Input layer, two hidden
layers & output layer
(Supervised MLP)
Input layer,
hidden
layer & output layer
Unipolar
function
sigmoid
Gradient
descent
learning algorithm
Input layer,
hidden
layer & output layer
Sigmoid &
function
LM
algorithm
&
Input layer, two hidden
layers (pattern , class) &
output layer
Membership
function
Asymmetric
Gaussian
function(general
shape)
Probability density
function (Bayesian
decision rule)
learning
Sigmoid function
Gradient
algorithm
type
Input layer, 2 hidden
layers
(pattern,
summation) & output
layer
Input layer,
hidden
layer & output layer
horizon
HSTDNN
EBP
WNN
One-shot
learning
algorithm
Gradient
descent
learning algorithm
PNN
Past decade years
Training set & test data
Decennial
(1999-2008)
Training set-50%
validation set-25%
test data-25%
Training data-80%,
testing data-20%
MMNN
GMDHNN
range
FLFNN
FLANN
FWNN
LLWNN
MNN
PbNN
PNN
WNN
7 year stock data for
Nasdaq-100 index &
4 year stock data for
NIFTY index
EBP
Input layer, one or more
hidden layer & output
layer
Input layer, atleast one
hidden layer & output
layer
Input layer, 2 hidden
layers & output layer
Flat net without any
hidden layer
Flat net without any
hidden layer
Polynomial transfer
function
Trigonometric
function
Trigonometric
function
Training Testing
NSE
1200
400
INFY 2000
400
BSE
1600
400
DE & BP
Training data-950 data
Diagnostic testing data-50
data
Training data-50%,
test data-50%
DE
Input layer, 5 hidden
layers & output layer
Gaussian function
EDA
Input layer, atleast one
hidden layer, output
layer
Gaussian function
morphological neural network with
modified genetic algorithm
Modular neural network
Probabilistic neural network
Procedural neural network
Weightless neural network
© 2013 ACEEE
DOI: 03.LSCS.2013.2.531
linear
BP
EBP
NSE:01-03-2000 to
30-03-2012
INFY:30-03-2000 to
23-03-2012
BSE:26-02-1992 to
15-10-2008
Last 3 years of stock
data
Sigmoid function
or logistic function
COMPARATIVE STUDIES
ANFIS
ANN
ARMA
BNN
BPFLANN
78
Adaptive neuro fuzzy inference system
Artificial neural network
Autoregressive moving average
Backpropogation neural network
Back propagation based Functional link
artificial neural model

7. Tutorial Paper
Proc. of Int. Conf. on Control, Communication and Power Engineering 2013
BPN
CBR
DEFLANN
DEFWNN
FLANN
FNN
GAFWNN
GMM
GPA
HGUTLN
HMM
MACD
MLP
MRA
MRLTAEF
RNN
SES
SVM
TAFE
TIMTAFE
TLFN
TTDNN
WaNN
Back propagation network
Case based reasoning
Differential evolution based Functional
link artificial neural model
Differential evolution using fuzzy
wavelet neural network
Functional link artificial neural model
Feed-forward neural network
Genetic algorithm using fuzzy wavelet
neural network
Generalized methods of moments with
kalman filter
Genetic programming algorithm
Highly granular unsupervised time
lagged network
Hidden markov model
Moving average convergence /
divergence model
Multi layer perceptron
Multiple regression analysis
Morphological-rank linear time-delay
added evolutionary forecasting
Hybrid, mixed recurrent network
Single exponential smoothing
Support vector machine
Time-delay added evolutionary
forecasting
Translation invariant morphological
time-lag added evolutionary forecasting
Time lagged feed-forward network
Traditional time delay neural network
Wavelet neural network
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PERFORMANCE MEASUREMENT
AMAPE
ARV
CC
FPE
MAD
MAP
MAPE
MSE
NMSE
POCID
RMSE
R2
RR
TIC
Average mean absolute percentage
error
Average relative variance
Correlation coefficient
Final prediction error
Mean absolute deviation
Maximum absolute percentage error
Mean absolute percentage error
Mean squared error
Normalized mean squared error
Percentage of change in direction
Root mean squared error
Squared correlation
Rate of return
Theil inequality coefficient
REFERENCES
[1]Alhassan, J.K., Sanjay Misra.: Using a weightless neural network
to forecast stock prices: A case study of Nigerian stock
exchange. Scientific Research and Essays. 6(14) (2011) 29342940
[2]Andreas karathanasopoulos.: GP algorithm versus hybrid mixed
© 2013 ACEEE
DOI: 03.LSCS.2013.2.531
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