1、CCITT RECMNxE.506 (REV*1) 92 m 4862591 0573695 746 m INTERNATIONAL TELECOMMUNICATION UNION CCITT E.506 (rev.1) THE INTERNATIONAL TELEGRAPH AND TELEPHONE CONSULTATIVE COMMITTEE TELEPHONE NETWORK AND ISDN QUALITY OF SERVICE, NETWORK MANAGEMENT AND TRAFFIC ENGINEERING FORECASTING INTERNATIONAL TRAFFIC
2、Recommendation E.506 (rev.1) Geneva, 1992 - CCITT RECMN*E-SOb (REV*L) 92 m 4862593 0573b7b 682 m INTERNATIONAL TELECOMMUNICATION UNION CCITT E.506 (rev.1) THE INTERNATIONAL TELEGRAPH AND TELEPHONE CONSULTATIVE COMMITTEE TELEPHONE NETWORK AND ISDN QUALITY OF SERVICE, NETWORK MANAGEMENT AND TRAFFIC EN
3、GINEERING FORECASTING INTERNATIONAL TRAFFIC Recommendation ESQ6 (rev.1) Geneva, 1992 FOREWORD The CCITT (the In rnational Tel graph and Telephone Consultative Committ e) is a permanent organ of the International Telecommunication Union (ITU). CCIT is responsible for studying technical, operating and
4、 tariff questions and issuing Recommendations on them with a view to standardizing telecommunications on a worldwide basis. The Plenary Assembly of CCIT which meets every four years, establishes the topics for study and approves Recommendations prepared by its Study Groups. The approval of Recommend
5、ations by the members of CCIT between Plenary Assemblies is covered by the procedure laid down in CCITT Resolution No. 2 (Melbourne, 1988). Recommendation ES06 was prepared by Study Group II and was approved under the Resolution No. 2 procedure on the 16th of June 1992. CCIT NOTE In this Recommendat
6、ion, the expression “Administration” is used for conciseness to indicate both a telecommunication Administration and a recognized private operating agency. O ITU 1992 All rights reserved. No part of this publication may be reproduced or utilized in any form or by any means, electronic or mechanical,
7、 including photocopying and microfilm, without permission in writing from the ITU. CCITT REC!N*E.SOb (REVS11 92 m 4862593 0573698 455 m Recommendation ES06 FORECASTING INTERNATIONAL TRAFFIC) (revised 1992) 1 Introduction This Recommendation is the first in a series of three Recommendations that cove
8、r intemational telecommunications forecasting. In the operation and administration of the international telephone network, proper and successful development depends to a large degree upon estimates for the future. Accordingly, for the planning of equipment and circuit provision and of telephone plan
9、t investments, it is necessary that Administrations forecast the traffic which the network will carry. In view of the heavy capital investments in the international network, the economic importance of the most reliable forecast is evident. The purpose of this Recommendation is to give guidance on so
10、me of the prerequisites for forecasting international telecommunications traffic. Base data, not only traffic and call data but also economic, social and demographic data, are of vital importance for forecasting. These data series may be incomplete; strategies are recommended for dealing with missin
11、g data. Different forecasting approaches are presented including direct and composite methods, matrix forecasting, and top down and bottom up procedures. Recommendation ES07 provides guidelines for building forecasting models and contains an overview of various forecasting techniques. Recommendation
12、 ES08 covers the forecasting of new international telecommunica- tions services. 2 Base data for forecasting An output of the international traffic forecasting process is the estimated number of circuits required for each period in the forecast horizon. To obtain these values, traffic engineering te
13、chniques are applied to forecast erlangs, a measure of traffic. Figure lB.506 outlines two different approaches for determining forecasted erlangs. The two different strategies for forecasting are the direct strategy and the composite strategy. The first step in either process is to collect raw data
14、. These raw data, perhaps adjusted, will be the base data used to generate the traffic forecasts. Base data may be hourly, daily, monthly, quarterly, or annual. Most Administrations use monthly accounting data for forecasting purposes. With the direct strategy, the traffic carried in erlangs, or mea
15、sured usage, for each relation would be regarded as the base data in forecasting traffic growth. These data may be adjusted to account for such occurrences as regeneration (see Recommendation E.500). In both strategies (direct and composite) it is necessary to convert the carried traffic into offere
16、d traffic erlangs. The conversion formula can be found in Recommendation ES01 for the direct strategy and in this Recommendation for the composite strategy. The old Recommendation E.506 which appeared in the Red Book was split into two Recommendations, revised ES06 and new ES07 and considerable new
17、material was added to both. Recommendation ES06 1 Explanatory variables I I 1 * Forecast Conversion to offered load . -b Accounting minutes I Conversion to offered load Forecast * - Sizing circuits) b + (number of Direct strategy TO200800 Measured usage b Forecast b Conversion to offered load Explan
18、atory variables U FIGURE 1/E.506 Direct and composite strategy Composite forecasting uses historical international accounting data of monthly paid minute traffic as the base data. The data may be adjusted by a number of factors, either before or after the forecasting process, that are used for conve
19、rting paid minutes on the basis of the accountingdata into busy-hour erlang forecasts. As seen in Figure 1B.506, the forecasting process is common to both the direct and composite strategy. However, the actual methods or models used in the process vary. Forecasts can be generated, for example, using
20、 traffic matrix methods (see 8 4), econometric models or autoregressive models (see 8 3, Recommendation E.507). There are various other data that are input to the forecasting process. Examples of these are explanatory variables, market segmentation information and price elasticities. Wherever possib
21、le, both the direct and composite forecasting strategies should be used and compared. This comparison may reveal irregularities not evident from the use of only one method. Where these are significant, in particular in the case of the busy hour, the causes for the differences should be identified be
22、fore the resulting forecast is adopted. In econometric modelling especially, explanatory variables are used to forecast international traffic. Some of the most important of these variables are the following: - exports, - imports, 2 Recommendation ES06 CCITT RECMN*E.SOb (REV%L) 92 4862593 0573700 933
23、 - degree of automation, - quality of service, - time differences between countries, - tariffs, - consumer price index, and - gross national product. Other explanatory variables, such as foreign business travellers and nationals living in other countries, may also be important to consider. It is rec
24、ommended that data bases for explanatory variables should be as comprehensive as possible to provide more information to the forecasting process. Forecasts may be based on market segmentation. Base data may be segmented, for example, along regional lines, by business, non-business, or by type of ser
25、vice. Price elasticities should also be examined, if possible, to quantify the impact of tariffs on the forecasting data. 3 Composite strategy - Conversion method Where both composite and direct strategies are being used and, in any other case, where past data is available, an overall paid-minute/er
26、lang ratio should be derived based on the present value for the particular relation, observed trends and future objectives. If data is not available, the conversion should be carried out in accordance with the formula. A = Mdhl60e (3- 1) where A M is the monthly paid-minutes, d is day-to-month ratio
27、, h e is the efficiency factor. is the estimated mean traffic in the busy hour, is the busy hour-to-day ratio, and The formula is described in detail in Annex A. 4 Procedures for traffic matrix forecasting 4.1 Introduction To use traffic matrix or point-to-point forecasts the following procedures ma
28、y be used: - direct point-to-point forecasts, - Kruithofs method, - extension of Kruithofs method, - weighted least squares method. It is also possible to develop a Kalman filter model for point-to-point traffic taking into account the aggregated forecasts. Tu and Pack describe such a model in 161.
29、The forecasting procedures can be used to produce forecasts of internal traffic within groups of countries, for example, the Nordic countries. Another application is to produce forecasts for national traffic on various levels. Recommendation ES06 3 _ _._ _ - - - -_ .- CCITT RECNN*E.SOb (REV*1) 92 W
30、4862591 0573701 7T 4.2 Direct point-to-point forecasts It is possible to produce better forecasts for accumulated trafk than forecast of traffic on a lower level. Hence, forecasts of outgoing trflic (row sum) or incoming traffic (column sum) between one country and a In this situation it is possible
31、 to adjust the individual forecasts by taking into account the aggregated On the other hand, if the forecasts of the different elements in the traffic matrix turn out to be as good as the Evaluation of the relative precision of forecasts may be carried out by comparing the ratios one with missing ob
32、servations t 1 2 3 4 5 6 7 8 9 10 Xf 100 112 125 140 152 - - - 206 221 Yt 300 338 380 422 460 496 532 574 622 670 The last known observation of xt before the gap at time 5 is 152, while the first known observation after the gap at time 9 is 206. Hence r = 5 and k = 3. The calculation gives: 496 - 46
33、0 36 - 622 - 460 - 162 A6 = 532 - 460 12 622 - 460 = 162 A7 = 574 - 460 114 622 - 460 - 162 - Ag = 36 162 26 = 152 4- - (206 - 152) = 164 72 162 27 = 152 + - (206 - 152) = 176 114 162 $8 = 152 + - (206 - 152) = 190 6.3 Modifcation of forecasting models The other possibility for handling missing obse
34、rvations is to extend the forecasting models with specific procedures. When observations are missing, a modified procedure, instead of the ordinary forecasting model, is used to estimate the traffic. To illustrate such a procedure we look at simple exponential smoothing. The simple exponential smoot
35、hing model is expressed by: = (1 - a) yt + abt-1 6-31 Recommendation ES06 7 _ - _ - CCITT RECMN*E.506 (REV*l) 72 = 4862571 0573705 415 where y1 is the measured traffic at time t, fit is the estimated level at time t, a is the discount factor and (1 -a) is the smoothing parameter. Equation (6-3) is a
36、 recursive formula. The recursion starts at time 1 and ends at n if no observation is missing. Then a one step ahead forecast is given by: If some observations lying in between 1 and n are missing, then it is necessary to modify the recursion procedure. Suppose now that yl, y2, . . ., yr, yr+k+l yr+
37、k+28 . . ., y, are known and yr+l, yr+2, . . ., Y or number of Says and social interest calculations for the relation and month; or objectives for tariff and promotion programmes to reduce day to day variations. It should be noted that where non-weekday traffic exceeds weekday traffic, it may be des
38、irable to change the forecasting and dimensioning base to take this into account. A.2.4 Busy-hourlday ratio (h) The relative amount of average weekday traffic in the busy-hour primarily depends on the difference between the local time at origin and destination. Moderately successful attempt have bee
39、n made to predict the diurnal distribution of traffic based on this information together with supposed “degree of convenience” at origin and destination. However, sufficient discrepancies exist to warrant measuring the diurnal distribution, from which the busy-hour/day ratio may be calculated. Where
40、 measurement data is not available, a good starting point is Recommendation E.523. From the theoretical distributions found in Recommendation E.523, one finds variations in the busy-hour/day ratio from 10% for O to 2 hours time difference and up to 13.5% for 7 hours time difference. This ratio is in
41、fluenced by subscriber perceptions of quality and tariff policies. For some applications it may be desirable to choose a value based on the objectives for service improvement, traffic promotion or tariff programmes. Based on experience in the long-term this could reduce h to 6% or less. A.2.5 EfJici
42、ency factor (e) The efficiency factor (ratio of busy-hour paid time to busy-hour occupied time) e converts the paid time into a measure of total circuit occupancy. There is a tendency for the efficiency to change with time. In this regard, efficiency is mainly a function of operating method (manual,
43、 semi-automatic, international subscriber dialling), in the B subscribers availability, and the quality of the distant network. Forecasts of the efficiency can be made on the basis of extrapolation of past trends together with adjustments for planned improvements. The detailed consideration of effic
44、iency including measurements is also an advantage from an operational viewpoint in that it may be possible to identify Improvements that may be made, and quantify the benefits deriving from such improvements. For automatic working with modem signalling systems, e can attain values in excess of 0.9.
45、A.2.6 Mean offered busy hour trafic It should be noted that A is the mean offered busy-hour traffic expressed in erlangs. Offered traffic can be approximated by: - - considering it equal to carried traffic (where blocking is not significant or is unknown): or using the methods of Recommendation E.50
46、1. A.3 Overall ratios The detailed conversion described above provides insight into the factors influencing total traffic efficiency and increases the accuracy of short-term forecasts. 10 Recommendation ES06 CCITT RECMN*E.SOb (REV*iL) 92 W 48b259L 0573708 I124 For long-term forecasts and other appli
47、cations where detailed measurements are not available or inappropriate, it is sufficient to consider “typical“ or target values for the overall erlang to paid-minute ratio. These might range from l/lOOOO for inefficient existing international/relations in 1990, to 1/25000 as a long-term objective fo
48、r more efficient relations. For some applications it might be practical to do long-term forecasts directly on the basis of circuit to paid- minute ratios. B.l Telex data ANNEXB (to Recommendation E.506) Example using weighted least squares method The telex traffic between the following countries has
49、 been ankjzed: - Denmark(DNK) - USAWSA) - Finland(FIN) - Nonvay(N0R) - Sweden(S). The data consists of yearly observations from 1973 to 1984 19. B.2 Forecasting Before using the weighted least squares method, separate forecasts for the traffic matrix have to be made. In this example a simple ARMA (0,2,1) model with logarithmic transformed observations without explanatory variables is used for forecasting. It may be possible to develop better forecasting models for the telex traffic between the various countries. However, the main point in this example o
