WebSite Visit Forecasting Using Data Mining Techniques
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This research paper explores the application of data mining techniques for predicting website visits, focusing on time series regression methods to forecast time-dependent data points. The study employs various regression algorithms, including Gaussian processes and SMO regression, tested on a dataset from Google Analytics, to improve prediction accuracy for web traffic, which is particularly beneficial for website owners in developing marketing strategies. Results indicate that SMO regression and linear regression provide the most accurate forecasts, highlighting the importance of data preprocessing in achieving reliable predictions.
![INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 12, DECEMBER 2013
ISSN 2277-8616
Web Site Visit Forecasting Using Data Mining
Techniques
Chandana Napagoda
Abstract: Data mining is a technique which is used for identifying relationships between various large amounts of data in many areas including scientific
research, business planning, traffic analysis, clinical trial data mining etc. This research will be researching applicability of data mining techniques in web
site visit prediction domain. Here we will be concentrating on time series regression techniques which will be used to analyse and forecast time
dependent data points. Then how those techniques will be applied to forecast web site visits will be explained.
Keywords: Forecasting, Web Site, SMO Regression, Linear Regression, Gaussian Regression and Multilayer Perceptron
————————————————————
I. INTRODUCTION
II. REVIEW OF LITERATURE
In contemporary world with the technological changes, ability
of predicting web site visiting patterns have a significance
value for every site owner for targeting their business for right
customers at right time. The available solutions to predict
future access and usage patterns for web sites are Business
Intelligence (BI) tools. But for many small and medium sized
companies or web site owners are unable to afford for them
since they are expensive solutions. The available BI tools
including Google Analytic doesn't support forecasting features.
They are focused on analysing user behaviours on the web
sites and log related results. So the solution going to be
developed from this research is targeted on web site owners
and administrators to assist the future predictions of web site
visits on their marketing strategies. For instance in software
products related companies, they need to know the visiting
history for their sites to plan their new releases, upgrades, etc.
Another case is density of forecast visitors would be helpful to
allocate or deallocates servers. All the above requirements
exist in web site visit prediction domain motivated me on this
research work for providing a suitable solution implementation.
The aim of this research work is applying a suitable
forecasting technique to predict web site visits. Thus, the
results derived through forecasting will be assist web site
owners in,
Forecasting is analysing and predicting the future behaviour of
the selected data set. In Forecasting knowledge from the
analysed data is used to predict future behaviours. It help in
many ways in various domains such as controlling load
balance, future marketing campaigns, allocating or deallocating resources and caching ,prefetching web pages for
improve performance. There are limited number of researches
have been done on Web site related forecasting. D. Ciobanu,
C. E. Dinuca done a research for Predicting the next page to
be visited by a web user[1]. They have created a java program,
using Net Beans IDE, which calculates the probability of
visiting the pages using the page rank algorithm and counting
links. The approach was using web site log analysis to
determine probabilities of visiting the pages. Their concept
founded from the web page ranking algorithm Page Rank [2].
Taowei Wang, Yibo Ren completed a research on the
suggesting methodology for personalized recommendation
using collaborative filtering. Their system architecture and
details on data preparation described in the research [3]. For
improving the quality of personal recommendation, they have
proposed a new personalized recommendation model which
takes the good consideration of URL related analysis and
combines the K-means algorithm. They have shown proposed
model is effective and can enhance the performance of
recommendation through results. Web Log Mining by an
Improved AprioriAll algorithm research done by WANG tong
HE Pi-lian shows that the possibility and importance on
applying Data Mining technologies in Web log mining and also
emphasizes some problems in the conventional searching
engines. Further they offer an improved algorithm based on
the original AprioriAll algorithm, which has been used in Web,
logs mining widely. Test results show the improved algorithm
has a lower complexity of time and space [5]. Other than the
above researches already done on web related predications it
has been mentioned by many researchers that their future
research areas will be focused on forecasting area [4]. The
wide usage of the Internet in various fields has increased the
automatic extraction of the log data from the web sites. The
usage of data mining techniques on those data collected from
the web helps us in pattern selection, which acts as a
traditional way of decision-making tools. But with availability of
current technologies we can use data from Application such as
Google Analytics, and Alexa for pattern identification and
forecasting.
Predict total number of visitors within next WEEK time.
Predict number of visitors on a given DAY (Sunday,
Monday, etc) within next WEEK time.
The objectives of this research work for achieving the aim are,
Identifying and investigating data mining techniques
which can be used for time series data forecasting.
and applying suitable pre-processing
techniques to clean the data.
How the identified techniques can be applied in web site
visit predication domain
Identifying
___________________________
Chandana Napagoda
Department of Computer Science, University of
Moratuwa Katubedda, Sri Lanka
cnapagoda@gmail.com
170
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![INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 12, DECEMBER 2013
III. PREPROCESSING
Data per-processing is an impotent step in data mining. It
helps to remove garbage information effect from initial data set.
It intends to reduce some noises, incomplete and inconsistent
data. The results from pre-processing step can be later
processed by data mining algorithms. In a data set of web site
visits, marketing or related activities can create a sudden
increase in number of visitors. But it won't retain for a longer
time period. In this type of a situation that sudden increased
visitor count data is considered as outliers.
A. Handling Outliers
The best way to remove outliers is organizing similar values in
to group/ cluster. Outliers can be identified through human or
computer inspection. But used human inspection to identify
outliers patterns. For example consider a situation where
mean page view count is 121, but some days has page view
amount as 518. In this case, for removing the outliers fist
calculate mean value and standard deviation (is the square
root of variance). In a normal distribution curve, from μ–σ to
μ+σ contains about 68% of the measurements. There for if
the forecasting doesn't interested about outliers, the values
which are out of the μ–σ to μ+σ range will be removed and
assigned with mean value.
IV.FORECASTING TECHNIQUES
The data present in the environment of web site usage
predication is time dependent series of data points. Modeling
and explaining such data using statistical techniques is 'Time
series analysis’. The process of using a model to forecast
future events based on known past events is 'Time series
forecasting'. Time series data such as web site access usage
data has a natural temporal ordering. Typically in data mining
applications, each data point is an independent example of the
concept to be learned and the ordering of data points within
the set doesn't matter. But for time series data it is not the
case. So one approach of handling time series data is
removing its temporal ordering so that standard propositional
learning algorithms can process them. Here when removing
the temporal ordering, the time dependent data should be
encoded via additional input fields. These fields are known as
'lagged' variables. After data has been transformed, regression
algorithms can be applied to learn a model. One approach is
to apply multiple linear regressions. Also any method which is
capable of predicting target can be applied. Lagged variables
are the main mechanism by which the relationship between
past and current values of a series can be captured by
propositional learning algorithms[11]. In this research we used
minimum lag value as 1 and maximum lag value as 14.
A. Weka
Weka (Waikato Environment for Knowledge Analysis) is a
popular suite of machine learning software written in Java,
developed at the University of Waikato, New Zealand [6].
Weka (>= 3.7.3) has an environment which is dedicated for
time series analysis. It allows creating forecasting models,
evaluating them and visualizing them. The approach of time
series analysis in Weka is transforming the data into form that
standard propositional learning algorithms can process. As
above mentioned Weka does this by removing the temporal
ordering of individual input examples by encoding the time
dependent via additional input fields. Various other fields are
also computed automatically to allow the algorithms to model
ISSN 2277-8616
trends and seasonality. After the data has been transformed,
any of Weka's regression algorithms can be applied to learn a
model. One approach is to apply multiple linear regressions.
Also Weka allows any method capable of predicting a
continuous target. For example support vector machine (SVM)
for regression trees and model trees can be applied. Model
trees are decision trees with linear regression at the leaves.
Weka is consist with multiple classifier functions and in this
research four of those classifier functions will be used. They
are namely Gaussian Process, Multilayer Perceptron, Linear
Regression and SMO Regression.
1. Gaussian Process
This classifier function implements Gaussian process for
regression without hyper-parameter tuning. Here the missing
values are replaced with global mean/mode values [7].
2. Multilayer Perceptron
This classifier function uses backpropagation to classify
instances. This network can be built by hand, created by an
algorithm or both. The network can also be monitored and
modified during training time [8].
3. Linear Regression
This function uses linear regression for predication. Weighted
instances are possible to be used with this approach [9].
4. SMO Regression
Support vector classifier will be trained using polynomial or
RBF kernels where John C. Platt's sequential minimal
optimization algorithm is implemented here [10].
V. WEB SITE VISIT FORECASTING
This paper discusses about the Web site visitors forecasting
for a web site owned by one of a Software Company. For this
purpose we have used daily page views for the web site from
the Google Analytics from 01 April 2011 to 22 July 2012 time
range. Here a number of different prediction approaches have
been applied such as Gaussian processes, linear regression,
multilayer perceptron and SMO regression which are available
on Weka time series analysis environment.
A. Date Set
We have consecutive 476 days of page view information which
are downloaded from Google Analytic. The same page view
data set cannot be used in prediction modelling and model
validation both scenarios. Therefore information on last 14
days has been removed from the input data set and that data
set is used to validate output of the prediction. The input data
set is with only one attribute which is daily page view. Further
the input data set will be used in forecasting in two different
ways. Data set with outliers and data set without outliers are
used in forecasting and variation of the forecast results will be
evaluated along with the data set.
B. Forecasting Execution Environment
In this research, Waikato Environment for Knowledge
Analysis-version 3.7.6 (Weka 3.7.6) tool and a package called
“Time series forecasting environment” are being used for
performing the predication modelling and predicting[11]. The
input data set prepossessing and Attribute-Relation File
Format (ARFF) generation steps are done manually and “Time
171
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![INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 12, DECEMBER 2013
TABLE IV
PERFORMANCE COMPARISON OF FORECASTING RESULT WITH
OUTLIERS
MAE
MSE
RMSE
RBE
RSE
SMO
Regression
4.
643
3411
58.
403
0.
0318
-23.
36
Gaussian
Processes
3.
071
4617
67.
9482
-0.
0182
-27.
48
Linear
Regression
16.
143
7848
88.
589
1.
0761
523.
2
Multilayer
Perceptron
319.
786
3766
961
1940.
866
0.
074
883.
01
ISSN 2277-8616
Linear Regression, performing outlier’s analysis is a very
important pre-processing step which produces more accurate
results. By comparing the results which are shown in Table 3
and Table 4, SMO Regression and Linear Regression are the
best suited methods for predicting or forecasting Web site visit
related information. When using Linear Regression,
performing outlier’s analysis is a very important pre-processing
step which produces more accurate results.
VI.CONCLUSIONS
The accuracy of the above forecast results has been analysed
and evaluated based on Mean Squared Error (MSE), Mean
Absolute Error (MAE), Rooted Mean Squared Error (RMSE),
Relative Mean Squared Error (RMSE) and Relative Absolute
Error (RAE). Table 3 and Table 4 are shown with results of
accuracy for each of the four forecasting methods used. Actual
visit and forecasted graph representation shown in Fig. 1.
This paper had discussed about the Web site visitors
forecasting for a web site owned by one of a Software
Company. The data present in this environment is time
dependent series of data points. Modeling and forecasting in
this environment is Time Series analysis and Time Series
Forecasting. So the paper had discussed about applicability of
Weka tool which provides a knowledge analysis environment.
Basically the research went along four regression algorithms
which are applied against the data set with outliers and without
outliers. So how the data pre-processing, particular algorithm
will assist in achieving accurate results were evaluated end of
the paper based on the derived results. Based on the
evaluation results we can conclude that SMO regression and
Linear Regression algorithms are better suited for forecasting
web site related information and also using Linear Regression
on pre-processed data gives more accurate results. Further
when forecasting is done on web site related information, we
can reduce various web hosting and server maintenance cost
through prior acknowledging about future events. This
research can be extended further with applicability of
forecasting and data smoothing technology like Exponential
smoothing.
ACKNOWLEDGMENT
We wish to acknowledge Mark Hall for developing Weka “Time
series forecasting environment” package and given me advice
we conduct this research.
180
160
140
REFERENCES
120
[1]
D. Ciobanu, C. E. Dinuca, “Predicting the next
page that will be visited by a web surfer using
Page Rank algorithm,” in International Journal of
Computers and Communications, 2012, pp.60-67
[2]
Z. Markov, D. T. Larose, Data Mining The Web
Uncovering Patterns in Web Content, Structure
and Usage. USA: John Wiley & Sons, 2007.
[3]
100
T. Wang and Y. Ren, “Research on personalized
recommendation based on web usage mining
using collaborative filtering technique,” WSEAS
Trans. Info. Sci. and App., vol. 6, no. 1, pp. 62–72,
Jan. 2009.
[4]
X. Wang, A. Abraham, and K. A. Smith, “Intelligent
web traffic mining and analysis,” J. Netw. Comput.
Appl., vol. 28, no. 2, pp. 147–165, Apr. 2005.
[5]
W. Tong and H. Pi-lian, Web Log Mining by an
Improved AprioriAll Algorithm, ;in Proc. WEC (2),
2005, pp.97-100.
SMO
80
60
40
Gaussian
Linear
20
0
Actual
Fig. 1 A line graph representation of forecast result wit actual
visit
The mean squared error and the mean absolute error shows
how forecast result and actual output are closer. The relative
mean squared error and relative absolute error are used as a
good measure of accuracy. By comparing the results which
are shown in Table 3 and Table 4, SMO Regression and
Linear Regression are the best suited methods for predicting
or forecasting Web site visit related information. When using
173
IJSTR©2013
www.ijstr.org](https://image.slidesharecdn.com/web-site-visit-forecasting-using-data-mining-techniques-131219231731-phpapp01/85/WebSite-Visit-Forecasting-Using-Data-Mining-Techniques-4-320.jpg)
![INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 12, DECEMBER 2013
[6]
I. H. Witten, E. Frank, and M. A. Hall, Data Mining:
Practical Machine Learning Tools and Techniques,
Third Edition, 3rd ed. Morgan Kaufmann, 2011.
[7]
K. Driessens, “GaussianProcesses.” [Online].
Available:
http://weka.sourceforge.net/doc.dev/index.html?we
ka/classifiers/functions/GaussianProcesses.html.
[Accessed: 6-Aug-2012].
[8]
M.
Ware,
“MultilayerPerceptron.”
[Online].
Available:
http://weka.sourceforge.net/doc/weka/classifiers/fu
nctions/MultilayerPerceptron.html. [Accessed: 8Aug-2012].
[9]
ISSN 2277-8616
E. Frank and L. Trigg, “LinearRegression.”
[Online].
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nctions/LinearRegression.html. [Accessed: 10Aug-2012].
[10] E. Frank, L. Shane, and S. Inglis, “SMO.” [Online].
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with Weka - Pentaho Data Mining.” [Online].
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+Series+Analysis+and+Forecasting+with+Weka.
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174
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WebSite Visit Forecasting Using Data Mining Techniques
- 1. INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 12, DECEMBER 2013 ISSN 2277-8616 Web Site Visit Forecasting Using Data Mining Techniques Chandana Napagoda Abstract: Data mining is a technique which is used for identifying relationships between various large amounts of data in many areas including scientific research, business planning, traffic analysis, clinical trial data mining etc. This research will be researching applicability of data mining techniques in web site visit prediction domain. Here we will be concentrating on time series regression techniques which will be used to analyse and forecast time dependent data points. Then how those techniques will be applied to forecast web site visits will be explained. Keywords: Forecasting, Web Site, SMO Regression, Linear Regression, Gaussian Regression and Multilayer Perceptron ———————————————————— I. INTRODUCTION II. REVIEW OF LITERATURE In contemporary world with the technological changes, ability of predicting web site visiting patterns have a significance value for every site owner for targeting their business for right customers at right time. The available solutions to predict future access and usage patterns for web sites are Business Intelligence (BI) tools. But for many small and medium sized companies or web site owners are unable to afford for them since they are expensive solutions. The available BI tools including Google Analytic doesn't support forecasting features. They are focused on analysing user behaviours on the web sites and log related results. So the solution going to be developed from this research is targeted on web site owners and administrators to assist the future predictions of web site visits on their marketing strategies. For instance in software products related companies, they need to know the visiting history for their sites to plan their new releases, upgrades, etc. Another case is density of forecast visitors would be helpful to allocate or deallocates servers. All the above requirements exist in web site visit prediction domain motivated me on this research work for providing a suitable solution implementation. The aim of this research work is applying a suitable forecasting technique to predict web site visits. Thus, the results derived through forecasting will be assist web site owners in, Forecasting is analysing and predicting the future behaviour of the selected data set. In Forecasting knowledge from the analysed data is used to predict future behaviours. It help in many ways in various domains such as controlling load balance, future marketing campaigns, allocating or deallocating resources and caching ,prefetching web pages for improve performance. There are limited number of researches have been done on Web site related forecasting. D. Ciobanu, C. E. Dinuca done a research for Predicting the next page to be visited by a web user[1]. They have created a java program, using Net Beans IDE, which calculates the probability of visiting the pages using the page rank algorithm and counting links. The approach was using web site log analysis to determine probabilities of visiting the pages. Their concept founded from the web page ranking algorithm Page Rank [2]. Taowei Wang, Yibo Ren completed a research on the suggesting methodology for personalized recommendation using collaborative filtering. Their system architecture and details on data preparation described in the research [3]. For improving the quality of personal recommendation, they have proposed a new personalized recommendation model which takes the good consideration of URL related analysis and combines the K-means algorithm. They have shown proposed model is effective and can enhance the performance of recommendation through results. Web Log Mining by an Improved AprioriAll algorithm research done by WANG tong HE Pi-lian shows that the possibility and importance on applying Data Mining technologies in Web log mining and also emphasizes some problems in the conventional searching engines. Further they offer an improved algorithm based on the original AprioriAll algorithm, which has been used in Web, logs mining widely. Test results show the improved algorithm has a lower complexity of time and space [5]. Other than the above researches already done on web related predications it has been mentioned by many researchers that their future research areas will be focused on forecasting area [4]. The wide usage of the Internet in various fields has increased the automatic extraction of the log data from the web sites. The usage of data mining techniques on those data collected from the web helps us in pattern selection, which acts as a traditional way of decision-making tools. But with availability of current technologies we can use data from Application such as Google Analytics, and Alexa for pattern identification and forecasting. Predict total number of visitors within next WEEK time. Predict number of visitors on a given DAY (Sunday, Monday, etc) within next WEEK time. The objectives of this research work for achieving the aim are, Identifying and investigating data mining techniques which can be used for time series data forecasting. and applying suitable pre-processing techniques to clean the data. How the identified techniques can be applied in web site visit predication domain Identifying ___________________________ Chandana Napagoda Department of Computer Science, University of Moratuwa Katubedda, Sri Lanka [email protected] 170 IJSTR©2013 www.ijstr.org
- 2. INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 12, DECEMBER 2013 III. PREPROCESSING Data per-processing is an impotent step in data mining. It helps to remove garbage information effect from initial data set. It intends to reduce some noises, incomplete and inconsistent data. The results from pre-processing step can be later processed by data mining algorithms. In a data set of web site visits, marketing or related activities can create a sudden increase in number of visitors. But it won't retain for a longer time period. In this type of a situation that sudden increased visitor count data is considered as outliers. A. Handling Outliers The best way to remove outliers is organizing similar values in to group/ cluster. Outliers can be identified through human or computer inspection. But used human inspection to identify outliers patterns. For example consider a situation where mean page view count is 121, but some days has page view amount as 518. In this case, for removing the outliers fist calculate mean value and standard deviation (is the square root of variance). In a normal distribution curve, from μ–σ to μ+σ contains about 68% of the measurements. There for if the forecasting doesn't interested about outliers, the values which are out of the μ–σ to μ+σ range will be removed and assigned with mean value. IV.FORECASTING TECHNIQUES The data present in the environment of web site usage predication is time dependent series of data points. Modeling and explaining such data using statistical techniques is 'Time series analysis’. The process of using a model to forecast future events based on known past events is 'Time series forecasting'. Time series data such as web site access usage data has a natural temporal ordering. Typically in data mining applications, each data point is an independent example of the concept to be learned and the ordering of data points within the set doesn't matter. But for time series data it is not the case. So one approach of handling time series data is removing its temporal ordering so that standard propositional learning algorithms can process them. Here when removing the temporal ordering, the time dependent data should be encoded via additional input fields. These fields are known as 'lagged' variables. After data has been transformed, regression algorithms can be applied to learn a model. One approach is to apply multiple linear regressions. Also any method which is capable of predicting target can be applied. Lagged variables are the main mechanism by which the relationship between past and current values of a series can be captured by propositional learning algorithms[11]. In this research we used minimum lag value as 1 and maximum lag value as 14. A. Weka Weka (Waikato Environment for Knowledge Analysis) is a popular suite of machine learning software written in Java, developed at the University of Waikato, New Zealand [6]. Weka (>= 3.7.3) has an environment which is dedicated for time series analysis. It allows creating forecasting models, evaluating them and visualizing them. The approach of time series analysis in Weka is transforming the data into form that standard propositional learning algorithms can process. As above mentioned Weka does this by removing the temporal ordering of individual input examples by encoding the time dependent via additional input fields. Various other fields are also computed automatically to allow the algorithms to model ISSN 2277-8616 trends and seasonality. After the data has been transformed, any of Weka's regression algorithms can be applied to learn a model. One approach is to apply multiple linear regressions. Also Weka allows any method capable of predicting a continuous target. For example support vector machine (SVM) for regression trees and model trees can be applied. Model trees are decision trees with linear regression at the leaves. Weka is consist with multiple classifier functions and in this research four of those classifier functions will be used. They are namely Gaussian Process, Multilayer Perceptron, Linear Regression and SMO Regression. 1. Gaussian Process This classifier function implements Gaussian process for regression without hyper-parameter tuning. Here the missing values are replaced with global mean/mode values [7]. 2. Multilayer Perceptron This classifier function uses backpropagation to classify instances. This network can be built by hand, created by an algorithm or both. The network can also be monitored and modified during training time [8]. 3. Linear Regression This function uses linear regression for predication. Weighted instances are possible to be used with this approach [9]. 4. SMO Regression Support vector classifier will be trained using polynomial or RBF kernels where John C. Platt's sequential minimal optimization algorithm is implemented here [10]. V. WEB SITE VISIT FORECASTING This paper discusses about the Web site visitors forecasting for a web site owned by one of a Software Company. For this purpose we have used daily page views for the web site from the Google Analytics from 01 April 2011 to 22 July 2012 time range. Here a number of different prediction approaches have been applied such as Gaussian processes, linear regression, multilayer perceptron and SMO regression which are available on Weka time series analysis environment. A. Date Set We have consecutive 476 days of page view information which are downloaded from Google Analytic. The same page view data set cannot be used in prediction modelling and model validation both scenarios. Therefore information on last 14 days has been removed from the input data set and that data set is used to validate output of the prediction. The input data set is with only one attribute which is daily page view. Further the input data set will be used in forecasting in two different ways. Data set with outliers and data set without outliers are used in forecasting and variation of the forecast results will be evaluated along with the data set. B. Forecasting Execution Environment In this research, Waikato Environment for Knowledge Analysis-version 3.7.6 (Weka 3.7.6) tool and a package called “Time series forecasting environment” are being used for performing the predication modelling and predicting[11]. The input data set prepossessing and Attribute-Relation File Format (ARFF) generation steps are done manually and “Time 171 IJSTR©2013 www.ijstr.org
- 3. INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 12, DECEMBER 2013 series forecasting environment” is used for forecasting. Figure 1 shows structure of ARFF file. @relation 'Analytics Visitors Overview' @attribute Visits numeric @attribute Day date 'MM/dd/yyyy' @data 99,4/1/2011 67,4/2/2011 72,4/3/2011 102,4/4/2011 110,4/5/2011 ISSN 2277-8616 TABLE II FORECASTING RESULT WITH ACTUAL VALUES WITH OUTLIERS SMO SMO Gaus sian Regre ssion Linear Reg Multil ayer Perce ptron Actu al Visit s 06/30/12 82 82 82 72 94 07/01/12 77 77 84 61 59 07/02/12 119 113 123 108 126 07/03/12 119 117 121 105 125 07/04/12 118 118 120 113 132 07/05/12 118 117 122 110 113 110 115 100 07/07/12 79 76 79 70 51 07/08/12 79 75 83 63 118 111 122 110 105 07/10/12 118 116 120 111 117 115 119 111 121 07/12/12 118 115 121 112 117 07/13/12 113 108 114 88 108 47 94 84 74 76 542 59 07/02/12 119 107 111 208 126 07/03/12 115 131 143 595 125 07/04/12 144 119 136 1086 132 07/05/12 128 150 152 1072 109 07/06/12 116 121 139 834 126 07/07/12 77 70 95 570 51 07/08/12 80 70 79 355 64 119 110 113 -64 105 07/10/12 122 129 145 -343 146 07/11/12 123 119 137 22 121 07/12/12 125 141 154 571 117 07/13/12 116 118 141 465 108 146 07/11/12 88 64 07/09/12 67 126 Actu al Visit s 109 07/06/12 80 07/09/12 TABLE I FORECASTING RESULT WITH ACTUAL VALUES WITHOUT OUTLIERS Multila yer Perce ptron 07/01/12 C. Forecast Results The result of the forecasted information is shown in the Table 1 and Table 2. Those results include values returned by applying each of the predication methods and actual page visit count. Hence result page visit information can be compared between each prediction methods; Gaussian, Linear regression, Multilayer Perceptron regression and SMO regression. As above mentioned this forecast has used page view information of 472 days. Linear Reg 06/30/12 Figure 1: View of Attribute-Relation File Format Gauss ian Regre ssion D. Results Evaluation TABLE III PERFORMANCE COMPARISON OF FORECASTING RESULT WITHOUT OUTLIERS MAE MSE RMSE RBE RSE SMO Regression 0.357 3003 54.779 -0.0019 -14.577 Gaussian Processes -2.237 2939 54.212 0.0096 -12.045 Linear Regression 3 3394 58.258 -0.01 -20.082 Multilayer Perceptron 10.643 4387 66.234 0.0295 -12.186 172 IJSTR©2013 www.ijstr.org
- 4. INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 12, DECEMBER 2013 TABLE IV PERFORMANCE COMPARISON OF FORECASTING RESULT WITH OUTLIERS MAE MSE RMSE RBE RSE SMO Regression 4. 643 3411 58. 403 0. 0318 -23. 36 Gaussian Processes 3. 071 4617 67. 9482 -0. 0182 -27. 48 Linear Regression 16. 143 7848 88. 589 1. 0761 523. 2 Multilayer Perceptron 319. 786 3766 961 1940. 866 0. 074 883. 01 ISSN 2277-8616 Linear Regression, performing outlier’s analysis is a very important pre-processing step which produces more accurate results. By comparing the results which are shown in Table 3 and Table 4, SMO Regression and Linear Regression are the best suited methods for predicting or forecasting Web site visit related information. When using Linear Regression, performing outlier’s analysis is a very important pre-processing step which produces more accurate results. VI.CONCLUSIONS The accuracy of the above forecast results has been analysed and evaluated based on Mean Squared Error (MSE), Mean Absolute Error (MAE), Rooted Mean Squared Error (RMSE), Relative Mean Squared Error (RMSE) and Relative Absolute Error (RAE). Table 3 and Table 4 are shown with results of accuracy for each of the four forecasting methods used. Actual visit and forecasted graph representation shown in Fig. 1. This paper had discussed about the Web site visitors forecasting for a web site owned by one of a Software Company. The data present in this environment is time dependent series of data points. Modeling and forecasting in this environment is Time Series analysis and Time Series Forecasting. So the paper had discussed about applicability of Weka tool which provides a knowledge analysis environment. Basically the research went along four regression algorithms which are applied against the data set with outliers and without outliers. So how the data pre-processing, particular algorithm will assist in achieving accurate results were evaluated end of the paper based on the derived results. Based on the evaluation results we can conclude that SMO regression and Linear Regression algorithms are better suited for forecasting web site related information and also using Linear Regression on pre-processed data gives more accurate results. Further when forecasting is done on web site related information, we can reduce various web hosting and server maintenance cost through prior acknowledging about future events. This research can be extended further with applicability of forecasting and data smoothing technology like Exponential smoothing. ACKNOWLEDGMENT We wish to acknowledge Mark Hall for developing Weka “Time series forecasting environment” package and given me advice we conduct this research. 180 160 140 REFERENCES 120 [1] D. Ciobanu, C. E. Dinuca, “Predicting the next page that will be visited by a web surfer using Page Rank algorithm,” in International Journal of Computers and Communications, 2012, pp.60-67 [2] Z. Markov, D. T. Larose, Data Mining The Web Uncovering Patterns in Web Content, Structure and Usage. USA: John Wiley & Sons, 2007. [3] 100 T. Wang and Y. Ren, “Research on personalized recommendation based on web usage mining using collaborative filtering technique,” WSEAS Trans. Info. Sci. and App., vol. 6, no. 1, pp. 62–72, Jan. 2009. [4] X. Wang, A. Abraham, and K. A. Smith, “Intelligent web traffic mining and analysis,” J. Netw. Comput. Appl., vol. 28, no. 2, pp. 147–165, Apr. 2005. [5] W. Tong and H. Pi-lian, Web Log Mining by an Improved AprioriAll Algorithm, ;in Proc. WEC (2), 2005, pp.97-100. SMO 80 60 40 Gaussian Linear 20 0 Actual Fig. 1 A line graph representation of forecast result wit actual visit The mean squared error and the mean absolute error shows how forecast result and actual output are closer. The relative mean squared error and relative absolute error are used as a good measure of accuracy. By comparing the results which are shown in Table 3 and Table 4, SMO Regression and Linear Regression are the best suited methods for predicting or forecasting Web site visit related information. When using 173 IJSTR©2013 www.ijstr.org
- 5. INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 2, ISSUE 12, DECEMBER 2013 [6] I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, 3rd ed. Morgan Kaufmann, 2011. [7] K. Driessens, “GaussianProcesses.” [Online]. Available: http://weka.sourceforge.net/doc.dev/index.html?we ka/classifiers/functions/GaussianProcesses.html. [Accessed: 6-Aug-2012]. [8] M. Ware, “MultilayerPerceptron.” [Online]. Available: http://weka.sourceforge.net/doc/weka/classifiers/fu nctions/MultilayerPerceptron.html. [Accessed: 8Aug-2012]. [9] ISSN 2277-8616 E. Frank and L. Trigg, “LinearRegression.” [Online]. Available: http://weka.sourceforge.net/doc/weka/classifiers/fu nctions/LinearRegression.html. [Accessed: 10Aug-2012]. [10] E. Frank, L. Shane, and S. Inglis, “SMO.” [Online]. Available: http://weka.sourceforge.net/doc/weka/classifiers/fu nctions/SMO.html. [Accessed: 7-Aug-2012]. [11] M. Hall, “Time Series Analysis and Forecasting with Weka - Pentaho Data Mining.” [Online]. Available: http://wiki.pentaho.com/display/DATAMINING/Time +Series+Analysis+and+Forecasting+with+Weka. [Accessed: 10-Aug-2012]. 174 IJSTR©2013 www.ijstr.org