An Intellectual Forecasting Support System

Copyright © IFAC Large Scale Systems. Beijing. PRC. 1992

AN INTELLECTUAL FORECASTING SUPPORT SYSTEM} Liu Bao, Wang Liang, Xu Demin and Niu Xiuli insliluJe ojSyslems Engineering. Tianjin University. 300072. Tianjin . PRC

Abst ract. Good for ecast wh ich i s necessary to al I kinds of effective decision can only be obtained by the help of an efficacious f orecasting support system. The system studied by us is an intellectual forecasting su pport system (IFSS) which can be used by both experts in forecasting and commmon users who want to get a good foreca st about something concerned without any knowledge and experience of for8casting technology. Th e functions of rFSS are: to choose the appropriate for ecas tin g mod el for users to cope with t he event to be foreca sted; to bui Id the time series mode l automatically if this type of model has be en selected; to improve the s yste m at any time by adding new models. extending model base. deleting. updating old ones and other ope rations; to examine the forecasting re s ults and choose the best one from th em. In this paper. the architecture and framework of IFSS are described and explained. the functional structure of IFSS is introduced. especially. automatic model ing subsystem. intellectual model selection and model management ~ubsystems arc emphasized. and then the implementation of IFSS and the use of software engnieering theory are discussed. Keywords. Fore cas ting theory; expert system; model base management; pattern recognition; identification; software engineering.

INTRODUCTION

foreca ste rs wi t h experience. The first and also the most important step in the procedure to carry out a forecast is the cho ice of a right forecasting method or model (Libert. Liu and Wang. 1092 ). Al I these forecasting models. softwares and systems ~hich contain several models do not generally give guides to users how to choose onc or several appropriate ones from a group of forecastinr, models or approaches to cope with the forecasted event.For most time series models.especially the ARIHA models. which usually ne ed expp.rienced foreca s ter s to bui Id the Model bas ed on the da ta about the event to be forecas ted . an easi Iy controlled and automatically operated modeling method is often wanted. Be s ides. the core part of n forecasting su pport sys tem is the model part.. The designer of IFSS mu st consider how to make a generalized model management system ~hich is indepe ndent of the overall controlling process so that the probl~ms of model representation and model sto rage in computer.the implementation of adding new model. inquiring. deleting. updating model ~nd co mbining existing m0dels to a n~~ complex model can bp. solv-

Forecasting is to estimate and predict something before it happens. and is to judge the outcom~ of a result which ·.ill occur in the future. A good for 0 casting is necessary to all kinds of effective decision. If better forecasting of the fu t ure development of an event can be obt.ained. t.he decisi on maker may rely on this forecast to make appropriate decision about this event. With the development of hu~an society and science and technology. fo r ecasting technology develops rapidly. But it depends on whether a moderate s ize foreca~ting s upport system which is co mpr ehe nsive. DPpl icable and easy to use by both experts and common users could be avai lable that the technology of forecasting wil I be ~idely eMployed.Thgre are so muny practical forecasting support systems or soft~ares on the market ( McGee and Beaulllont. 1986; Beaumer.t. 1988). but IIICSt of theM are designed for

'This work reported here was supported by the Higher Education Doctoral Foundation of China with brant 110 . 9005617

ed.

Aimed

247

at problems above. we. thp group 0f

~cci ~ ion

signed us model-centered, and is 0riH' " by .",del.

and Forecasting in the Institute of Systems Engineering of TianJin University,havu been involved in several decision and forecasting research projects since 1987, and have been supported by China HHSF, China High Education Doctoral Foundation and Fork l.T. Foundation. This vuper', lIith a title of fin Intellectual Forecastittg Support System" (lFSS) , is a summary of purt of the outcome~ uf uur res~arches.

Dependpnt three basps txpc IFSS is a system of three bases type in which 0'0;)""\ base is the ccnter, and the whole sy~tem is driven by model.Knowledge base is vartly used tu implement t.he automatic bui Idin~ of tilD(; :""ries lIIodeh.vurtIy empluyed to choose 1II0del intellectually. Data base is to prov i de da la r"qu i ""d Lo eXP<:u te ,"odc·l, and te .~mori,e the resulLs uf Illude I ex ,",c uti"n . Thc" logic lIodel. .hich provides index informuLiuns of entity model. connects mod.·1 base with d~tu part, thus duta becomes '-' part of model b"~,,. Threp kinds of bases are interdependent. not independent.

IFSS is a large-scale, practical forecasting softvare system vhich is designed vith the combination of the COllputer technology,artificial intell igence, expert system and forecasting Eet~udolo~y. IFSS is a flexible forecasting support system, which can help both experts vho know forecasting technology deeply and common users who just want to obtain a g~od forecast about the evettl concerned without understanding any kind of forecasting t ec:, nology .

THE FUNCTIONAL STRUCTURE OF IFSS figure I shows th e framewor h of IFSS. Th i:; s\!d i(,,1. th" fun c tion,, 1 str u<:L ure will be ~iven, and ,,11 the subsystems ure exp[ain..,d ,e s pectiv\!ly. I~~S is consist"d of five IIIBin function,,1 ,; ubsystems, n
THE OVERfill DES IGH AND FRA~EWORK OF IFSS The uverall design of B large-scale software system is th~ basis oi softvare develoving on which the succ~ss or failure of a system is hinged. The developing pattern of overall design is thut an avplication need or requirement drives an overall design definition which in turn drives technolo~y which mayor may not be able to support the requirements. IFSS is an applicable system to help forecasting, and it belongs to the scope of DSS . Therefore, the overall design of I~SS should reflect the fundamenlHI pattern and requirement of DSS. Here we tend to that presented by Sprague(19801. The frHmework of IFSS is shown in Fig. I. Its characteristic are as fo Ilows.

Automatic Hqdel inK Subsystem

/

model is one of the musl. imporLilnl. fl, .. " cusling models by US" of Lime s.!!·ies models.A~IHi\ model is to date the most perfect forec ~st ing model in principl e . In case where an expert or,,~ uut 0mntic procedure is avai I~ble and the random~ess uf dat,-, is smal I, the ~RIHA model muy be more "pvrDPriate than other candidate ( liber·t, Liu and 'Jang. 1992). The purpose of the automatic mudel ing SuLsysLem in IFSS is to bui Id ARrHA w0del automatical Iy ~hen the conditions uui Iding such kind of mouel a,'e met. Liu and 'Jang ( 1089) st udied the str'ucturl2 id ~ nlifi­ cation problem of ARrHA ~cd01 by pattern recogniticn approach which consists ef t~,"ec st eps: patterns vector s vf ARl~fI models ~ru u Lt~ i n e J ~y the ESACFlexlend dd sample autocorr elatiu n fun~ti0n) proposed by Tsa y and Tiao(1974 ); Ji ~criffiin~ "t s (,f' vBrious training s amples ~,.~ s p~ c ified by th ~ percpption "Igor-ithm used in m~cf,inl 1"~I'"in.:;Ak;,jke's Ale and SIC criteria are introduc~d to c~oose the final stuct ure (A kaike, 1974). ~ang1l991)ext~nded th" method of structure identification to ARIHA model by empl oying GPACr (g(:ner~1 izeJ parli;,,j autocur-r-clution function) ~t.ere the model pattc,"n is es timated Ly Har quart non-linear least S4uare me thod. ARJ~A

Jnt.,llectyal AI techni4ue and expert system theory are introduced into the system to "nab I e the I FSS to he I p common users to choose models intellectually and build ARI~A models automatic&lly. Reflected in Fir, . I, the knowledge base, mar,,,gement system for knowledge base and infe, "ence mechini s m Ore builded (~ang, liu and Xu, 1990; Wang, 1991). Model-oriented Model plays an important rule in IFSS.The d~~ign 0f IFSS is model-centered, so model ~anagement syst~m i~ the core for the whole software SYsteM. It is wel I knuvn that a good information system should be designed around what we perceive as the stable entity of the system (Konsynski ~nd Sprague, 1986). Hany systems have been as process-centered or datacentered, but all these designs arc not verY successful. Comparing to "process" and "data",model is more stable for a definite sy~tem, model determines the relation bet~een data. Thus IFSS is de-

IntellectYal Hodd Scledic[i Sybsi , tc,uflHSS) There Hr c two kinds of model selection in the forecasting support system. One is called human uperated

248

"Ly le. whe ....:: eXller i"'Il:ed forcc"s Ler ~hoosc:; 1Il0de I dire~Lly from the model b3se by his ! her judgement. the oth~r is the type of intellectual and automatic zodt,} selection which is operated by intellectual ~odel selectio~ subsystem. This subsystem is implewonted by the combination of Al technique. theory of expert system and forecasting technolugy.

bel' lI a I tared .'II.d l"~ lnlll'.f''J r"nCy of th,; ,;Y:dl:llI I.ll usors will be i Dlpr·U'Ied . By a ~enpl'a I i .:e
In this section.Lhe basic principle of intellectual 1II0dol selecLion wi II be briefly explained. The knowledge and experience of experts and rules concerned h3ve been widely collecterl and an31yzed by W3ng(1989) and Libert. Liu and Welllg 11992). sever31 criteria for model selection h&ve also been pruposeJ: accuracy criterion. ubjectivity criterion. siwlllicity criterion. cost criterioll. mOLive criterion (I.ibert. Liu und 'hns.1992J .·Jhen yuU wan~ to cioou!>e ;1 IQOuel by HISS. ufter interaction betwc,en cumlllon users and IMSS.the inference Dn~ine b3sed on th~ rules in kno~ledge baSE processe s thn inferenc e action. fin311y, the HISS rell orL Lo us~r the finul chuice of ~odel selected.This subsystem has abil ity Lu "xplain its oction. tell user its vrocess of inference and provide help informutions to users. In order to r'aise the efficao 0f mod~1 selection. a fun~tional module fur data prepr0ce5sin g is affixed to the subsystem where the tellJeflO. the kind of tendency and lIeriodiciLY of original dat3 are anaIy~ed by stutistical test. and where the outcomes of an31ysis al'e interpolated intu the dyn3mic data base ~o that it cun be used or referl'ed when the model is selected (Wang. 1992) . Figure is the structure of intel le~tual model selection subsystem.

schemes: G"uffriun (1087) presenLed a method of structur<'d muJeling. lllanning (1986) Lended to bOITOW the re Ia t i ona! da ta base theol'Y and pr'eserlted ~J s\:heme cal led ~rltily-r0IaLillll ffiuLll()(i su that the model base is u counterpart of data Luse. und some p~uple I'uprespnt mudel usill~ t.he mcth0ds ",hich are uS0d to r ' e~I·"s,,,,t kr,,,~I()Jl!e in 1\1. The~;e sL'heme~ ill'e not very sutisfi"d. [11 t.his PJper. " well-st.ructured model representatiun scheme which i~ in combination with scm;Jlltic lid and fr'&mf' repres(~nLation. ,,'id borTows the desi".;n of th~ fi le ~ystem in " UNIX oper~ting systrm i~ propus~dlsee Fig. 3). This scheme is DlOl'e supen"r Lo Li,aL presented 01 Geoffrion (1987) in cumput.er imvlementation. In fig. 3. lIIod(,1 i~ r~pr~sl:nted I.i"rdrchically. The first hiurarchy is the ~en()ric tree. which is used to classify cliff"r"nt fore~"stinK Diode Is. The branch node repres~nt ~ " c luss uf mudel. while the leaf noJe i:; the specific model. The l'OOt node is for'ecasting model. its two chi IJ nodes represent twu main killds of forecusting models respectively: the objertivr mudel and the subjective mudel'. Undl,r <;ul.ju·liv" mud " I. there are differenL models such as individuul j.dgemelltal forecH5tin~.group furerasting.combined judgemental forecasting. Undel' obje ctive model. subclassifications are I istcJ uS extrapol"tin~ models. cau:;31 muJels Jnd ~ome uthrrs.ColltinueJ in this way. 3 generic tree ~bout for cc Hslin~ moJel~ is form ed. It is noted that different principles ue applied to Ll"ssify models. In ~enerat. we classify models by the scupe of models. However. this principle i· not very effective La classify causal model. 50 the principle th,.L ('very node und er causal model repr esA llts different 31~urithm is employed.Thus Lh~ casual model part is not only a module of the whole system. but an indepenJent s ystem which can be taken out from the whole system.

At present,

Hod,,1 Honagl'rnenl SlIb'yslrrnIHHS) The wain difference bctweell DSS und other informatiun systems is that the former' has "mud,"1 part. How to represent alld store model in cumputer affects directly the easiness of system implementutiun and the efficiency of model execution. ~ud~1 mainten3n~e is necessary to ens ure correcL execution and high efficiency of mudels. There are large quantity of different foreca, ;ting models in the model base of IFSS. These models have their own functions which are different from one 3nother. The input d3ta formats and numbel's required when differE-nt models are in execution
I'

there i:u 'e Sever'al

model

r't:!presentCttioo

Logic model which is corresponding onc bi 0ne to the leaf nod,"s uf generic Lr'ce is the ;Jbstract repres~ntation of the currespondillS entity model . It stores concerncJ index infurmations of entity model. All the logic moJels furm logic !Dodel base through some storing ~erhanism.

2Here.approaches.models 3~d methuds wil I be referred Lo as models. although they huve different implications. for example.lhe subje~tive forecasting model. in fact. is an approilch ur a m<.:thodology anJ not a specific model.

249

compul~tion,

the part of inl e lle ctua l model choice i s implemented by using artificial intell ignp.ce langu3ge- --Pr o log,the overal I control I ing subsystem and model manageme nt subsysle m are accompl is hed by prevalent and powerful language C. In ord e r to modul es by differ ent programming execu te these language alternati vely,and to inherit the executi ng envir onment, it is not fea sible to use the m ~t hod of sub program cal ling . Here th e t ec hnique of proce ss - sc hed ul ing is adopted and the memory is manag ed 0ffe cti ve ly so as to so lv e t he probl em of memory uns uffi c iency. By us ing the dy nami c data s lru ctur l' a nd th e method of memory appl ication,the general ity of t he s yste m is imp r oved and th e independ ence between mode l manag e me nl su bsyste m and the ovecal I co ntr ol I ing s ubsyste m is implemented.

I\n ent ity Mdel inc lu des lIl()d~1 s ke leton, model I/O datn and help inform~ti o ns. Mode l skeleton is an executing progran module.AI I the entity models f ora ent ity model ba se. In mod~1 base, data part is inc luded, this refle cts the c haracteri sti c of th e ove ra II frame~ork of lESS, ie , the three type bases are in ter depend e nt. Bas ed on this sc heme of repr ese nt a t io n, a genera li ze d mode l manag e men t s ubsys t e m whi ch is independen t of ove ra II co ntro II i rg process is set up ( sce Fig. 4), It can implellent mode l execution a nd be eas y t o augment a nd maintain model ba se . When addin g a new model. the corresponding tr ee n'o de s hou ld be added in generic tree a nd th e logic mode l s hould also be Dui Ided in lop-ic model ba se be s ides the additio n of entity mode l in entity model ba se; whil e mode l del e tion is wan ted ,en tity mode l is de le ted by software s ystem autom3ti cal Iy after the logic mode l is deleted, and then th~ co rresponding t re e node s hould be rem oved. Mode l execution is implemented by finding logi c modrl through ge neric tre e, th en callinf, enlity mode l. Besides, use r can eas ily inquir e th e fun ct ion and usage of model s in mode l base .

IFSS is a l a rg ,~ - sca l e ap pl icabl e so f t war e system co nsist ing of multiple fu nctional modules. The ma in idea of soft war e engineering s hou ld be fol lowed duri ng th e pr oced ur e of developing th e sn ftw Dre. Th ~ co ncept of so ftware and so ftware I if~ peri odicit y mu s t be under s to od c lea rly . The st ru ct ured pr og rammin g de s ign meth od whi ch i ~ from to p to nottom. modular and improving st~p by s tep mu st be adorted ( I~ ant; and Tang, 1990 ). Wh en developing so f l wa r e, t h" ord er is from bottom to top so that the s ysle m st ru ct ure ca n be ~xpressed clea rly.Aft e r these have bee n done,the effeclivity. r ei iabi I it:y,maint a in3biI ity and und cr 5tand abi I it.y can be ensll r ed.

m3naf,e ment s ubsyste m ca n impl eme nt mude l by user, mode l execu t ion and model maintenance. It bec omes the co r e of t he whol e forec as ting syst,"m . Mode l

c h oi~~

The Forecilst Analy ·i ng Sub s ys te m Th e

of f oreca sts is co n b ~ display~d for ecas ting c urv e can be the tendency and us e t he

~ nd

d~ta

COIICl.US ION

stored i n an output fil e wh e n this is n e~de d. The drawn to he lp user obse rv e datu of for ecasts .

By the re sear ch of IFSS, lhe fol l owing 0ut cn mcs Jre obl.a in ed. I. Th e r~ndom l im p. se r ies models can be hui Ided aut.omatica lly by us ing the met hod of GP AC F, thus co mputing time is reduced and the manul ope r ations are less than before.

The O.I'cal I Cpulcpl I jn" S"b 'i v'iJe m Thi s s ubs ys tem provid es use rs wi t h friendly manma c hine interfac ~ and man ages the executing of the oV01'all sy st.em . 'Jhen IFSS is executing,th e pull-down mcnU h and pOP-UP menu s wiJ I hel p users to com pl e te for cc3st.i ng proc ess. Thp input,display and up dating of d~ta nepded by model exec ution ar c pr ovided by t he emp Ly table, and the tran sl a t ion fr om use r da ta file to syst.e m d ~ta file i~ e-omplet~d by lESS au tomilt. ica II y.

2. AI,exp ert system and the mode l se l ~ c tio n crit0ria are co mbined to aecomp l ish the intel I cctu~ 1 model se lection. I t is conven ien t. for comm on u spr~ to f nrecast somet hi ng co nce r ned. 3. A gen eral ized mode l management s ub s ystem wh ic h is indl,pendent of overnll cont r o l I i ng pr De- eS!; is desig ned to implement the choi ce of m od~ 1 by use r hi~ se lf ! herself and th e model ma i nt. e nance . The arch il c ct.uf~ of IoIHS in t hi s pap er i~ r;onve ni l' nt for use rs t o extend and mnin tia n the sY~Lem.

SYSTEH IMPLEM~"TATJOH AND THE IiSE OF SOFTIIARE EHGINEE RING

4. Based on the basic pat l~ rn of DSS. th e fram ework of TFSS whi ch is intellectual, mode l-orient ed and th ree ba ses t.y pe is given .

IFS S is a complex Syst.~ ~,which is c hara ~tc ri ~tic of mll !tiple functions,multiple pr nce dur ns and mult.iple bases st r ucture. Module s of t.h e Systeffi ar c programmd by different. prop:ram langu ages, models i n lodel ha ~e use Fo r tran which is lood at mat he matical

5. During the pro cess of developing sys te m and Lhe r esearc h of soft ware syste m, some a dvnncc ~ imrll' ~en t i ng techn i que s s lI c h as proe-e<;s sch"d'J I i ne , PI'O-

250

gr~m overl~pping.

improves

to

end

software

eng i nee,. i ng

·.yslom.

In G.Z.Xul[d.). Sej"oljfj,' ') .'c j-; j·/n wd 'y'lrm> Engjn " "r·jn. Chir,a S"iellc0 ar:J Ti,chnolo~y I)ub -

the system power and

shows the higher level of prolramming. Frum n i ng

Lill Ba ·) . 119UO) . Comrr"hensiv,' for",,,,,.! in,;

program automatic Keneration etc.

arc employed. This

bcgin-

lishine Co. Beijirn;. "1'.1 >IG.

is app 1 i cd in

1;0

r~hir., ..; "I.

Liu Bdo and 'Ja"g Li"n~ 11~~'91 .!. ?:Jtl"II: ",c·; u,itiun n~pr0a\~h for' idpht i fy ir;t~ !\~~,it\ ~'! :""lL:! S;.~ ·IJ(·I U!'I" : ,

system design. Therefo,.~.

ing

the comprehensive intell ed ual for Lcas tsupport system provid e~; b0th f.Jr('c:,stin~ "x-

pert, and common u: .er·S with"

J> ~ \/"r'ful

fo r'ecastin>~

accu r"" y and efficiency of

i ll~.Ti d "jin

mpan,. Thc:

H~hlloud

are improved.

However. there arc some I i mit." I i OHS: Know I "u~e h" ~,, management system is primitive.anJ it needs improving. The addition and updating of kn o wledg e can not

\le

kr,owledoc

ILlse t o

~ome

~ !0. ~.

7~-H:~

framewvd, f,,,' Li ,.: H: f :

'S'j:ilt:!!1

11.1 i:1 "

""v" l ul'm l''''

'; U'j['I,

[,Ix, IJql

I-~G. (i.I'

autocorrelat ion nonsLaLiunary

11974). C'JIl'.i,.Lerri.

I'~'arnil ,'~

Cunct iOll

I\~HA

1'0'

S'.. dt

i,,,r:rr')

muciels.J. rd' Am ;'!';!'",

:;,,,j

'\,[1 :':

tics ASSQ('j-,t joo.La. 8\-%. liangLiuIIg. 119891. 0" th~ ch"ic" "ff
and

fr it'ndly IWane;. 1984) .

Systems ElIgin~ering .'It Ti"".iir, U,
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forecasting

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~"n~

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m()d('lill~

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choic" of fcrt'cast;;: :: mo(kl.,.

re,,'

fu,'

sys tefl,

If,

lime

for

the

MS li';",i';. Ir.::.li-

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}SrR

OVERALL CDHTRO~LL-:-IN-"'G"""P""'ROCl::§"""'-"~

pig. 1.

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r - -_ _ _

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Konsynsk i. B. R. and Spr·ague. R.11. 11 n6 I. Future

Liun e.

irul· I~-

gt'ncralized Je,:isi0n support SY'.I ,'IlI.I''['E lrJo::, S y s \ fh n r ~ Ill'. V0 I . 1·1. 11 0 ~. 7 UI - 7 I I.

Vo 1. 2. Geoffrion.Arther H.. (1987) .An introduction to structured model ing. Hilnr,g0m en! Scirnce. Vol. 33.

Wan~

and ils int01Ie('lu,,1

j \dl Dud Sy)t('illj FrlsjC)llCr'j Q"',

Vo1.7. 110 . 2.139-143. Blanning.R . W. (1986) .An entity-r'elalionship approach to model management. DecisiQn Support

mod,,1

mcnbtion. In G.Z.Xu 11:,1.). :; '·j .' r, ' jfj\, [I" " j'

Akilike.II.(1974).A new look ut the statistical model identification. IEEF Tran') on or. UL 110-123. Beaumont.C. (1988) .Software reviow.J. of forrcasting.

Libert.G .• Liu BaD and

li" (hi ,, -

of aut0rl"~~resive par,lmet'~r<~ ~lld t~;.:lenri{·d soIi1plp

solving

OSS. which is po we rful. genend ized

v{ / !. ),

SU PP0rt

Ts"y. R.S ,,,,J T;"G

of these problems musl make IFSS become u much more ~;ucc()ssful

f)iJ'(·!'jl·:t it '~' 1

·1.1'0. 1.

,,,tent;

have t o be added by HMS.These are our

research directions in the fuLure. and the

Ti~nj i ".I ' .f~.i'

HcG ,,,' an,j C.Il";";,,,·n t. 1I ~)8c)

. V. E

oC upcis:url

the ou",,. prui.>l e m is th"l o"ly Lhe: Lime ,.cri.:;. moJel,; can be builded by s ystem "utoruatically. the olher models

E

Sprague.R.H.(I~eO).A

bc' implemi,nted without .manual uP0r·ati ons. Thi~ pre-

vents e;;t" nsion of

Univu:.ity.

cs.') .

Overall framework of I F'SS

251

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Scheme of model representation in IFSS

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Fig.4 Structure of model management subsystem

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