A METHODOLOGY OF FORECASTING DEMANDS OF THE

COMMUNICATION TRAFFIC

Masayuki Higuma

NEC Corporation

5-7-1 Shiba Minato-KU Tokyo, Japan

Masao J.Matsumoto

Kyusyu Sangyo University, Japan

2-3-1 Shoukoudai Higashi-KU Fukuoka, Japan

Keywords:

Modeling,Framework,Demand forecast,Statistical analysis, society , economy, Simulation.

Abstract:

The Trafﬁc demand of the communication has strong relations to the gross domestic product (GDP). Some

statistical models are well known for the demand forecast. As such models, there are the Linear regression

Model (LM) and the Auto Regression model (AR). However the LM cannot apply analyzing a trafﬁc demand,

because its relations between a GDP and a trafﬁc demand have the non linear shape. Also the AR has problems

,which cannot reﬂect the impact of social and economical events ,and have big forecasting errors, because a

trafﬁc demand has a trend component. Therefore this study considers new a methodology of forecasting

demands of the communication trafﬁc, which has high quality by resolving the above problems, by modeling

and evaluating social and economical events.

1 INTRODUCTION

In recent years, skills of the demand forecast are more

important for telecommunication operator companies.

The mistake of judgements on their investments can

be made by errors of forecasting. Then we study new

a methodology of forecasting demands of the commu-

nication trafﬁc, for solving such problems.

The trafﬁc demand of the communication has

strong relations to the gross domestic product (GDP).

Some statistical models are well known for the de-

mand forecast. As such models, there are the Lin-

ear regression Model (LM) and the Auto Regression

model (AR)(Nakazawa, 2004). However the LM can-

not apply analyzing trafﬁc demands, because its re-

lations have the non linear shape. Also the AR has

problems ,which cannot reﬂect the impact of social

and economical events ,and has big forecasting er-

rors, because a trafﬁc demand has a trend component.

Therefore this study considers new a demand forecast

model of trafﬁc demands, which has high quality by

resolving the above problems, by modeling and eval-

uating social and economical events.

Our model has a parameter evaluated impacts of eco-

nomical and social events, and forecasts trafﬁc de-

mands with a GDP and such evaluated parameters.

However our study does not aim at predicting social

and economical events. Also, this study aims at im-

plementing on a system forecasting the trafﬁc demand

with information technologies, however this is only

our private work.

Firstly, this article describes well known models and

merits of this study. Secondly, our “K model” and “K

parameter” is presented. Thirdly, this article analyzes

that trafﬁc demands are driven by the GDP growth

and social and economical events. Fourthly, this ar-

ticle shows that these events can be evaluated with

the content analysis(Janis, 1965)(Krippendorf, 1980),

and an evaluated score has strong correlations to a K

parameter. Finally, conclusions and future study is-

sues are described.

2 WELL KNOWN

METHODOLOGIES

As statistical analyzing correlations between quan-

titative variables, there are the Auto Regression

Model (AR) and the Linear Regression Model

(LM)(Nakazawa, 2004) which can be applied fore-

casting trafﬁc demands. The AR and the LM has the

next weak points for forecasting trafﬁc demands.

1. The AR has the next problems.

(a) There are big errors forecasted trafﬁc demands,

482

Higuma M. and J.Matsumoto M. (2005).

A METHODOLOGY OF FORECASTING DEMANDS OF THE COMMUNICATION TRAFFIC.

In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 482-485

DOI: 10.5220/0002528104820485

 SciTePress

when targeted variables contain trend compo-

nents.

(b) The impact of social and economical issues can-

not be reﬂected.

2. The LM has the next problems.

(a) There are big errors forecasted trafﬁc demands

with the LM, because a correlation between the

trafﬁc demand and the Real GDP has a non linear

correlation.

(b) Forecasting with the LM must be computed over

the available section, then a forecasted trafﬁc de-

mand has no reliability.

Whereas, our new methodology can be reﬂected the

impact of social and economical events, and will have

smaller errors even if trafﬁc demands contain trend

components.

3 PRESENT OUR MODEL FOR

THE DEMAND FORECAST

This section describes our new model of forecasting

communication trafﬁc demands.

Our model for the demand forecast is deﬁned as the

next equations.

x(i) = x(i − 1) + K(i)Z(i) (1)

K(i) =

G(e

) (2)

i = 1, 2, . . . , n (3)

The above equation (1) is differential equation.

x(i) is a future value of x, x(i − 1) is a 1 order

past values of x(i), and K(i) means the liveliness of

the society, and is reﬂected the impact of social and

economical events. In the equation(2), the function

G(e

) evaluates social and economical events e

shown every events e

on every periods i. Also in the

equation(1), K(i) is called “K parameter”, because

the society in English means “syakai“ in Japanese,

then we get “K” in spelling of “syakai“. And, by the

same reason, this new model shown as the equation(1)

and equation(2) is called “K model”. Also, Z(i) is the

growth of the Real GDP.

In this K model, a future value x(i) is computed with

a 1 order past trafﬁc x(i − 1) and a K parameter K(i)

and an economical growth Z(i). Also, K(i) is com-

puted with the equation(2), and the function G(e

)

evaluates social and economical events. The function

G(e

) is deﬁned as the next formulas.

G(e

) =

) (4)

The g

) means evaluating some aspects

k of

with the content analysis(Janis, 1965)(Krippen-

dorf, 1980) and the text data mining(Hearst, 1999).

Also a

means weighted coefﬁcients. The value of

K(i) increases and decreases by happening social and

economical events e

with the equation(2) and the

equation(4). For example, K decrease by happening

fear events (eg.. the terrorism, the remarkable rise of

the crude oil), and increase by happening relief events

(eg..Olympic Games, the stabilization of the society).

4 EVALUATION

This section describes evaluating the above men-

tioned K model. This evaluation is used the telephone

switching trafﬁc data and GDP data in the USA, be-

cause these data can be gotten easily. Such trafﬁc data

is opened on the Internet during past 20 years.

Thus, we analyse a Dial Equipment Minutes

(DEM) of the Incumbent Local Exchange Carriers

(ILEC)(FCC, ) and the Real GDP chosen from the

National Economic Accounts(BEA, ) in the United

States of America (USA), from 1980 to 2002. Also

targeted K model is built on the R(TheR, 2004).(see

the next section)

4.1 Simulation

This section describes a simulation for the equation(1)

of the above mentioned K model on the R(TheR,

2004) shown as the next paragraph. At this simula-

tion, this K model is simulated from 1980 to 2001.

And a K parameter (shown as Fig.1) is computed and

assumed with a Real GDP and a DEM. By using this

K parameter, simulated DEM (shown as Fig.2)with

the R program (shown as the next paragraph) is good

matched to the real DEM. In this ﬁgure, the line is

simulated DEM, and the “x“ is the real DEM. Thus

we can describe past trends with this K parameter. It

is important to study relations among this K parame-

ter and events.

> Kmodel < − function(n){

+ for (i in 2:n){

+ Y [i] < − Y [i − 1] + K.ts[i] ∗ dGDP.ts[i]

+ }

+ return(Y )

+ }

4.2 Survey

This section describes surveying relations between

the K parameter and events which contain social and

eg..how widespread on the map, how deep impact on

the psychology

A METHODOLOGY OF FORECASTING DEMANDS OF THE COMMUNICATION TRAFFIC

483

The K parameter on the K model in this evaluating

year

K (minutes/dollars)

1985 1990 1995 2000

−20 0 20 40 60

Figure 1: The assumed K parameter on the K model in this

evaluating.

economical events. On this survey, we focus the rela-

tion among such K parameter and events gotten from

the Wikipedia(Wikipedia, ) at every years (Table.1).

Where, the information of these events is shown from

1982 to 1994, and written as the keyword only, be-

cause there is not enough space left for this article.

Also, dollar amounts of the ∆RealGDP on this ta-

ble is shown in million; ∆ means the differential op-

eration. Similarly, trafﬁc amounts of the ∆DEM is

shown in million minutes, and amounts of the K (this

is K parameter) is shown in minutes per dollars.

By analysing these data, it is discovered that the posi-

tion of the K is moved by the social and economical

events, then the DEM which is a communication traf-

ﬁc demand is moved too.

4.3 Evaluation of social and

economical events

In this section, it is veriﬁed that these events can be

evaluated with the content analysis(Janis, 1965)(Krip-

pendorf, 1980), and evaluated score has strong corre-

lations to the above K parameter.

Firstly, it is selected that some news of social

and economical events on the Wikipedia(Wikipedia,

) from 1980 to 2001. Such selected news are labeled

and weighted with two aspects:how wide spread on

the map (SOM), how deep impact on the psychology

(IOP). A SOM is weighted this way: 5 - in the world

wide, 4 - in the EU and USA, 3 - in the USA, 2 - on

the state in the USA, 1 - on the region except in the

USA, 0 - does not impact in the USA. Also, an IOP

is weighted this way: 5 - strong liveliness impacts,

Figure 2: The simulated DEM (shown as “line”) with the K

model in this evaluating.

0 - neutral, -5 deep fearful impacts. Then, the next

equation is evaluated with the score of such SOM and

IOP.

K(i) =

G(e

), G(e

) =

SOM

· IOP

(5)

An evaluated score

K(i) (this means a K para-

meter) is computed with equation(5). This results is

shown at Table.1.

Secondly, the correlation between the above evalu-

ated score

K(i)) and the assumed K parameter K(i)

statistically. Now the “null hypothesis” is assumed

that the above correlation coefﬁcient is zero. And

it is tested with statistical test method that such null

hypothesis is rejected. As a result, the probability

of the above null hypothesis is 4.958e

−07

, and the

correlation coefﬁcient is 0.87 with the peason test

method(Nakazawa, 2004). Otherwise, the probabil-

ity of the above null hypothesis is 0.002788 , and the

correlation coefﬁcient is 0.64 with the speaman test

method(Nakazawa, 2004). Then the above null hy-

pothesis is rejected.

5 CONSIDERATION

Firstly, as mentioned earlier, it is shown that the po-

sition of the communication trafﬁc demand is moved

by the social and economical events.

Also, some characteristics are distinguished from

the trend of ∆RealGDP , ∆DEM , K, and social

ICEIS 2005 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION

484

Table 1: Relations among the K parameter and events

year ∆RealGDP ∆DEM K Events SOM IOP

1982 -100975 65000 -0.46 The Falklands War 1 1 0.48

1983 212421 70000 0.33 the ’Star Wars 4 2 0.32

1984 372930 123000 0.33 Los Angeles Olympic 4 2 0.32

1985 212294 100000 0.47 Creation of Domain Name System 4 3 0.48

1986 195566 61000 0.31 Space Shuttle Challenger explodes 3 -2 -0.24

1987 200284 55000 0.27 Black Monday 4 -3 -0.48

1988 255612 124000 0.49 Canada & US FTA 4 2 0.32

1989 223272 74000 0.33 Tiananmen Square, Peking 1 -5 -0.2

1990 116196 37600 0.32 Iraq invades Kuwait 4 -3 -0.48

1991 -31570 30300 -0.96 Iraq accepts cease-ﬁre 5 3 0.6

1992 203779 90600 0.44 Aid Famine Relief In Somolia 4 3 0.48

1993 182184 121100 0.66 Internet in CBC TV 4 4 0.64

1994 284943 134300 0.47 800,000 Rwandans were killed 1 -3 -0.12

and economical events shown these Tables (Table.1).

Such characteristics are shown the next list.

1. If ∆RealGDP < 0 then If K < 0 then ∆DEM is

increased.

(a) The Falklands War from March to June 1982.

(b) ENDED: Iraq accepts cease-ﬁre 1991.

2. If ∆RealGDP > 0 then ∆DEM is moved by the

(a) from 1983 to 1990.

(b) from 1992 to 2000.

3. If ∆RealGDP > 0 then If K < 0 then ∆DEM is

decreased.

(a) Sept. 11 attacks on the World Trade Center.

Hence, we can point out the next issues from the

above results. If relief events have happened, the K

parameter increases. Otherwise, if fear events have

happened, the K parameter decreases.

Secondly, it is shown that the correlation between

an evaluated score

K(i) and an assumed K parame-

ter K(i) can be strongly. Consequently, if the above

model (equation(5)) is improved, we can make

K ≃

K. And, a K parameter on our K model can be eval-

uated with the content analysis (Janis, 1965)(Krip-

pendorf, 1980) from social and economical events on

news sources (eg..Wikipedia(Wikipedia, )).

6 CONCLUSION

Consequently, it is sure that the position of communi-

cation trafﬁc demands is moved by social and eco-

nomical events. This article analyzes the relation

among trafﬁc demands and social events and eco-

nomical events, and presents a “K model” (shown as

equation(1)) which can improve the reliability of fore-

casted trafﬁc demands with a “K parameter”. This

K parameter can be computed with evaluating so-

cial and economical events (shown as equation(2) and

equation(4)). Also the equation(4) can be realized

with the content analysis(Janis, 1965)(Krippendorf,

1980). Since there are many volumes and kinds in

social and economical events, sampling and coding

of these events should be resolved with the text data

mining(Hearst, 1999).

Finally, automating and improving the K model is

a theme in the next article.

REFERENCES

BEA. Bureau of economic analysis. WEB. URL

http://www.bea.gov/beahome.html.

FCC. Federal communications commission releases statis-

tics of communications common carriers. WEB. URL

http://www.fcc.gov.

Hearst (1999). Untangling text data mining. In the Pro-

ceedings of ACL’99: the 37th Annual Meeting of the

Association for Computational Linguistics. the Asso-

ciation for Computational Linguistics.

Janis (1965). The problem of validating content analysis. In

Language of Plitics. Cambridge: MIT press.

Krippendorf (1980). CONTENT ANALYSYS: An Introduc-

tion to its Methodology. Sage Publication, Inc., USA,

2nd edition.

Nakazawa (2004). An Introduction to Statistical Analysis

with R. Piason Education, Japan, 2nd edition.

TheR (2004). R: A language and environment for statistical

computing. R Development Core Team: R Foundation

for Statistical Computing, Vienna, Austria. ISBN 3-

900051-00-3.

Wikipedia. Wikipedia, the free encyclopedia. WEB. URL

http://en.wikipedia.org/wiki/1982.

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