FORECASTING TELECOMMUNICATION NEW SERVICE DEMAND BY ANALOGY METHOD AND COMBINED FORECAST

Transcription

1 Yugoslav Journal of Operatons Research 5 (005), Number, FORECAING ELECOMMUNICAION NEW ERVICE DEMAND BY ANALOGY MEHOD AND COMBINED FORECA Feng-Jenq LIN Department of Appled Economcs Natonal I-Lan Insttute of echnology aan, R.O.C. Receved: October 00 / Accepted: November 004 Abstract: In the modelng forecast feld, e are usually faced th the more dffcult problems of forecastng market demand for a ne servce or product. A ne servce or product s defned as that there s absence of hstorcal data n ths ne market. We hardly use models to execute the forecastng ork drectly. In the aan telecommuncaton ndustry, after lberalzaton n 996, there are many ne servces opened contnually. For optmal nvestment, t s necessary that the operators, ho have been granted the concessons and lcenses, forecast ths ne servce thn ther plannng process. hough there are some methods to solve or avod ths predcament, n ths paper, e ll propose one forecastng procedure that ntegrates the concept of analogy method and the dea of combned forecast to generate ne servce forecast. In ve of the above, the frst half of ths paper descrbes the procedure of analogy method and the approach of combned forecast, and the second half provdes the case of forecastng lo-ter phone demand n aan to llustrate ths procedure s feasblty. Keyords: Ne servce, lo-ter phone, analogy method, combned forecast, PH.. INRODUCION he absence of hstorcal data s the fundamental dfference beteen forecastng ne servces and forecastng the already exstng servces. For exstng telecommuncaton servces, there may be a substantal body of relatve hstorcal data nformaton on them has been bult up, hch can be dran upon for forecastng purposes. In contrast, only lmted nformaton s avalable concernng ne servces [].

2 98 F.-J. Ln / Forecastng elecommuncaton Ne ervce Demand by Analogy Method In order to make techno-economc forecasts for these servces, t becomes very mportant to establsh a reasonable forecastng procedure. In aan, after promotng the telecommuncatons lberalzaton n 996, there are several knd of ne telecommuncaton servces desred n the market. For satsfyng dfferent knd of demands, the DG (Drectorate General of ele-communcatons) n aan s contnung to open telecommuncaton servce markets, the lo-ter phone s one of the man servce tems. Based on estmated potental demand for ths ne servce, netork facltes and capactes may have to be establshed. herefore, t s necessary that the operators, ho have been granted the concessons and lcenses, forecast ths ne servce thn ther plannng process. Although lo-ter phone s the ne servce n aan, t s not global ne n the orld. For example, ths servce, called PH (Personal Handy-phone ystem), has already n exstence n Japan from July 995. hat s, there have hstorc data on other countres about ths servce. Hence, n ths paper, one reasonable forecastng procedure for lo-ter phone n aan based on analogy method and combned forecast s made up. he potental demand of ths ne servce s forecast, and forecasts are presented. In the ve above, n ths paper, the frst part descrbes the procedure and method of developng forecasts for a ne servce, hle the second part presents the lo-ter phone forecastng n aan usng ths techncal procedure.. A NEW PROCEDURE OF ANALOGY MEHOD he forecastng procedure of analogy method for a ne servce ll nvolve hstorcal data already n exstence n other countres, ts applcaton to the ne country and comparson of characterstcs beteen to countres. And the procedure of developng forecasts for a ne servce, nvolvng the combnatons of forecasts, s shon n Fgure. hs procedure can be descrbed as follong consecutve steps: tep : Collect the subscrber number of ths servce and relatve soco-economc data seres for other country that already n exstence. tep : Collect correspondng soco-economc data seres for ths ne country too. tep : Calculate ther relatonshp or converson rato beteen the subscrber number and soco-economc data for other country that are already n exstence. tep 4: Determne to construct ndependent and dfferent knd of models to the subscrber data usng soco-economc data, or tme seres models (such as polynomal trend model or exponental smoothng model) to the converson rato for other country already n exstence. tep 5: Estmate and Evaluate models. hs step s often called dagnostc checkng. he object s to fnd out ho ell the model fts the data. If each model s acceptable then go step 6, otherse back step 4 to reconsder other models. tep 6: Refer the soco-economc data on ne country, and take ths soco-economc data nto the sgnfcant models to generate ther on ntal forecasts, or estmate ther on converson ratos from dfferent knd of tme seres models. At the same tme, transfer ther ratos to ntal forecasts. Fnally, e use the method of combned forecast, descrbed n the thrd secton, to combne the dfferent knd of models forecasts to produce a eghted average forecast, called combned forecast.

3 F.-J. Ln / Forecastng elecommuncaton Ne ervce Demand by Analogy Method 99 Collectng Relatve Data for the Already Exstng Country Collectng Correspondng Data for ths Ne Country Data Collecton tage Calculatng or conversng Data for the Already Exstng Country Determnng the Dfferent Knds of Forecastng Methods Identfcaton of a entatve Model for Each Method Relatve heorem and Experence Model Constructon tage Model not Acceptable Estmaton of the parameters for the entatve Model Dagnostc Checkng for the entatve Model Confrmaton of the Optmal Model for Each Method Model Valdaton tage Computatons of the Combned Forecasts Adjustng Combned Forecasts by Other Informaton A Weghted-Average Algorthm and Crteron Informaton Integraton tage Fgure : A Ne Procedure of Developng Forecasts for Ne ervces

4 00 F.-J. Ln / Forecastng elecommuncaton Ne ervce Demand by Analogy Method tep 7: Adjust combned forecasts to fnal potental demand of ths ne servce. nce the combned forecasts are derved from techncal or structured models, sometmes, e can use market research or expert opnons to adjust these combned forecasts so to more the real potental demand of ths ne servce market nearly. herefore, the purpose of ths step s an attempt to model the decson process of judgmental forecastng revson n a structured approach. From above descrptons of man steps, e can fnd there are to very mportant assumptons that have to be consdered hen e use the analogy method to forecast the potental demand of a ne servce: () here have the most smlar soco-economc development trace to convert forecasts beteen these to countres. () he forecastng models, used n the procedure, have to follo ther on statstcal assumptons.. A MEHOD OF OBAINING HE COMBINED FORECA he usual approach to forecastng nvolves choosng a forecastng method among several canddates and usng that method to derve forecasts. Hoever, forecasts from one gven method may provde some useful nformaton hch s not handled n forecasts from the other methods. Hence, t seems reasonable to consder aggregatng nformaton by generatng forecasts from ndependent and dfferent knd of models, and then combnng these forecasts for one ne servce demand. In ths manner, the ultmate forecasts should contan more nformaton than s the case hen only a sngle model s used [8]. herefore, n ths secton, e consder that one combned forecast could be obtaned by a lnear combnaton of the k sets of forecasts, and these forecasts are derved from k dfferent knd of models. We gve a eght to the frst model set, a eght to the second model set, a eght to the thrd model set, and so on. hat s, the lnear combnaton s f c, = f, + f, + +(- k ) f k, = here f c, s the combned forecast at tme, f, s the forecast at tme from the frst model, f, s the forecast at tme from the second model, and f k, s the forecast at tme from the last model. here are many ays to determne these eghts. he problem s ho best to do t. In ths paper, e sh to choose a method that could yeld lo forecast errors for the combned forecasts. he varance of errors n the combned forecast c can be rtten as follong: c =Var ( c, f )=Var (, f + f + +(- ) f k, ), k =

5 F.-J. Ln / Forecastng elecommuncaton Ne ervce Demand by Analogy Method 0 If the forecasts are ndependent among these k ndependent and dfferent knd of models, then above formula could be rertten as follong: k c = + + +(- ) k = here s varance of the -th model. No, for mnmzng the combned varance, the above equaton can be dfferentated th respect to,,, k - ndvdually and equatng to zero, and e can get general eght as follongs: = = (... ) k Ck Π j j= k Ck Π j j= k k =,,, k- k = - k = In the case here k= and k=, e can rearrange as follongs: () When k =, ll be and ll be. + + () When k =, ll be, + + ll be, and ll be Usually, the true error varance practce, e can use under a gven model ll be unknon. In ME (mean of squared forecast errors) to estmate. 4. HE EMPIRICAL CAE In order to llustrate the feasblty of ths forecastng procedure for a ne servce, n ths secton, e ll refer to the groth trend of PH n Japan and use the forecastng procedure, descrbed n the second secton, to forecast the potental demand of lo-ter phone n aan. he practcal forecastng steps are descrbed as follongs: tep : Because e consder the aanese soco-economc envronment and telecommuncaton ndustry development very smlar as Japanese. We collect the frst three years subscrber data of PH, from July of 995 to June of 998, n Japan (shon n column of able ). At the same tme, e also collect the populaton n Japan (shon n column 4 of able ).

6 0 F.-J. Ln / Forecastng elecommuncaton Ne ervce Demand by Analogy Method tep : We collect the populaton (each half a year), from 995 to 999, n aan (shon n ro of ). tep : From the data of tep, e can calculate the PH penetraton rates n Japan (shon n column 5 of able ). tep 4: A plot of the PH penetraton rate data n Japan versus tme s gven n Fgure. From the groth curve pattern n Fgure, e can fnd that the penetraton rate slghtly declnes n the 8 th perod (Oct. of 997). But t seems stll reasonable to use or consder the thrd-order polynomal trend method, the trple exponental smoothng method, and the logstc regresson method to construct dfferent knd of models to PH penetraton rates n Japan. tep 5: No, e use the consdered methods n tep 4 and the PH penetraton rate data n able to construct dfferent knd of models. By model selectng process, three knd of optmal models are descrbed as follongs: A. he Frst Model: he hrd-order Polynomal rend Model he estmaton of the parameters n ths optmal trend model may be obtaned by usng regresson technques. he estmated model and relatve statstcs are: Penetraton rate(%) = tag tag ( 0.0) (46.545) ** ( 6.845) ** ME=0.098 R-QUARE=0.997 able : he No. of PH ubscrbers and Relatve Data n Japan year / tag month no. of subscrbers (thousands of unts) populaton (thousands of unts) the PH penetraton rates(%) combned forecasts of penetraton rates (%) 95/ , /08 0 5, /09 0 5, /0 60 5, / 480 5, / 60 5, /0 70 5, /0,00 5, /0,500 5, /04,070 5, /05,450 5, /06,80 5,70.5.8

7 F.-J. Ln / Forecastng elecommuncaton Ne ervce Demand by Analogy Method 0 able (Cont.) 96/07,0 5, /08,580 5, /09,950 5, /0 4,0 5, / 4,60 5, / 4,96 5, /0 5,66 5, /0 5,5 5, /0 6,00 5, /04 6,4 5, /05 6,655 5, /06 6,859 6, /07 6,965 6, /08 7,08 5, /09 7,068 6, /0 7,09 6, / 7,007 6, / 6,99 6, /0 6,94 6, /0 6,86 6, /0 6,78 6, /04 6,75 6, /05 6,65 6, /06 6,569 6, able : he Half a Year Populaton Data of aan Unt: thousands ag Year/month 95 /06 95 / 96 /06 96 / 97 /06 97 / 98 /06 98 / 99 /06 99 / populaton,4,04,87,47,577,68,777,870,95,04

8 04 F.-J. Ln / Forecastng elecommuncaton Ne ervce Demand by Analogy Method PH Penetraton Rate 6.00% 5.50% 5.00% 4.50% 4.00%.50%.00%.50%.00%.50%.00% 0.50% 0.00% Jul-95 ep-95 Nov-95 Jan-96 Mar-96 May-96 Jul-96 ep-96 Nov-96 Jan-97 Mar-97 May-97 Jul-97 ep-97 Nov-97 Jan-98 Mar-98 May-98 Month- Year Fgure : he Groth Curve of PH Penetraton Rate n Japan B. he econd Model: he rple Exponental moothng Model When e use a value of the smoothng constant equal to α = 0.05, e fnd that the mean of squared forecast errors for 6 observatons equals In a smlar manner, smulated forecastng of the penetraton rate data s carred out usng other values of the smoothng constant α. he mean of squared forecast errors for values of α beteen 0.05 and 0.99 n ncrements of 0.05 are gven n able. We fnd that α = 0.65 s the optmal value of the smoothng constant hen e use these penetraton rates to buld a trple exponental smoothng model. able : he ME for Dfferent Values of α α ME α ME α ME α ME Note: α s smoothng constant herefore, e can obtan updated values of the smoothed statstcs, [] and [] by usng follong smoothng equatons durng buldng ths trple exponental smoothng model: = 0.65 y [] = [] - = 0.65 [] +0.5 [] [] here y s the penetraton rate at tme, and -, smoothed statstcs computed at tme. - [] -, [] - are values of the

9 F.-J. Ln / Forecastng elecommuncaton Ne ervce Demand by Analogy Method 05 C. he hrd Model: he Logstc Regresson Model he logstc regresson curve s dfferent from the lnear and exponental curves by havng saturaton or celng level. herefore, e use non-lnear least squares teratve process to estmate ts parameters. he estmated model and relatve statstcs are Penetraton rate(%) = + exp( tag) ME=0.009 R-QUARE=0.998 tep 6: In ths step, follong the method of obtanng combned forecast descrbed n the thrd secton, e use eghted average to combne three models forecasts by ther estmated varances ME. hat s, the combned forecast ould be obtaned by a lnear combnaton of three sets of forecasts n ths case, gvng a eght to the frst model set, a eght to the second model set, and a eght = to the thrd model set. he lnear combnaton s f c, = f, + f, +( ) f, here f c, s the combned forecast at tme, f, s the forecast at tme from the frst model, f, s the forecast at tme from the second model, and f, s the forecast at tme from the thrd model. No, f the forecasts are ndependent among these three models, then for mnmzng the combned varance, as descrbed n the thrd secton, the above equaton can be dfferentated th respect to and ndvdually and equatng to zero, so that e can get eght, and as follongs: MEME = ME ME + ME ME + ME ME MEME = ME ME + ME ME + ME ME MEME = ME ME + ME ME + ME ME here ME s the estmated error varance of the -th model. From the formula, the eghts of these three models can be obtaned as follongs: =0.85, =0.6578, =0.607 then, the combned forecast at tme can be obtaned from follong: f c, =0.85 f, f, f, All of combned forecasts of the PH penetraton rate n Japan are obtaned and shon n column 6 of able.

10 06 F.-J. Ln / Forecastng elecommuncaton Ne ervce Demand by Analogy Method Although lo-ter phone servce operators have been granted the concessons and lcenses n 999 n aan, the formal operaton and servce as ated to for tll latter half of 00. And as descrbed before, e suppose that the aanese socoeconomc envronment and telecommuncaton ndustry development are smlar as Japanese. Hence, n our study, t s reasonable that e suppose the lo-ter phone penetraton rate n aan n January 00 to be equal to the PH penetraton rate n Japan n July 995. o carry on, e frst use the half a year populaton data of aan (shon n able ) to buld the follong frst-order trend model to predct the populaton from December 000 to June 00 (shon n column of able 4): Populaton = tag tag=,, (05.8) (8.484) ** And then, e transfer the estmated penetraton rates to lo-ter phone subscrber combned forecasts of aan n column 4 of able 4. able 4: he Lo-er Phone ubscrber Combned Forecasts of aan Year penetraton populaton(thousands of unts) ubscrbers tag / month rate loer95% forecast upper95% loer95% forecast upper95% estmate 00/ ,04,4,45 4, 4,44 4, / ,94,47,44 5,65 5,764 5, / ,5 56 5,80 5,97 54, / ,55 89,69 89,74 From able 4, n the frst half year of begnnng operaton, e can estmate that potental demand ll be 5 thousands at most by our techncal procedure. It s very close to actual 0 thousands subscrbers that the operator announced. And the frst year of begnnng operaton ll be about 5 thousands. tep 7: After gettng subscrber combned forecasts by our techncal forecastng procedure for obtanng fnal potental demand, e shall use market research or expert opnons to adjust these combned forecasts. Based on the fnal potental forecasts beng not our ultmate purpose of ths paper. Although e dd not do these to orks n ths case, e can judge drectly that the groth of lo-ter phone ll be affected by to factors. hey are () the scope of ts operaton and servce, () the fare s contnung to decrease and promoton alternatves s contnung to provde for moble phone n aan reless telecommuncaton market. herefore, n the begnnng operaton year of lo-tre phone, the 5 thousand subscrbers, e estmate, ll be the maxmum potental demand.

11 F.-J. Ln / Forecastng elecommuncaton Ne ervce Demand by Analogy Method CONCLUION In ths paper, e ntegrate the concept of analogy method and the dea of combned forecast to establsh the procedure of forecastng telecommuncaton ne servce demands. In ths context, e frst descrbe all the steps of the forecastng procedure, and then, e provde a ay to determne the eghts of obtanng one combned forecast that could yeld loer mean of squared forecast error. Fnally, for llustratng the feasblty of ths forecastng procedure for a ne servce, e forecast the potental demand of lo-ter phone n aan usng ths techncal forecastng procedure. he dea of combned forecast and analogy method are not ne. Hoever, n ths paper e ntegrate them n a ne ay and llustrate that t s feasble for developng ne servce forecasts. REFERENCE [] Boerman, B.L., and O Connell, R.., Forecastng and me eres: An Appled Approach, rd ed, Wadsorth, Inc., Belmont, Calforna, 99. [] CCI: Forecastng Ne elecommuncaton ervces, CCI Recommendaton E.508, [] Chang, H.J., and Ln, F.J., "Forecastng telecommuncaton traffc usng the enhanced stepse projecton multple regresson method", Yugoslav Journal of Operaton Research, 5 (995) [4] Chang, H.J., and Ln, F.J., "Forecastng th the enhanced stepse data adjustment regresson method", he Asa-Pacfc Journal of Operatonal Research, 5 (998) 5-8. [5] Dartos, J.P., and Gruszeck, M., "Demand forecastng for ne telecommuncaton servces", raffc Engneerng for IDN Desgn and Plannng, 6-7 (988). [6] Draper, N., and mth, H., Appled Regresson Analyss, nd ed., John Wley & ons, Inc., Ne York, 98. [7] Kmura, G. et al., "ervce demand forecastng and advanced access netork archtectures", IC, 4 (994) 0-0. [8] Makrdaks,., and Wnkler, R.L., "Averages of forecasts: ome emprcal results", Management cence, 9(9) (98) [9] tordahl, K., and Murphy, E., "Forecastng long-term demand for servces n the resdental market", IEEE Communcatons Magazne, () (995) [0] Wasem, O.J. et al., "Forecastng broadband demand beteen geographc area", IEEE Communcatons Magazne, () (995)