Invoicing and Financial Forecasting of Time and Amount of Corresponding Cash Inflow


 Meagan Stokes
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1 Dragan Smć Svetlana Smć Vasa Svrčevć Invocng and Fnancal Forecastng of Tme and Amount of Correspondng Cash Inflow Artcle Info:, Vol. 6 (2011), No. 3, pp Receved 13 Janyary 2011 Accepted 20 Aprl 2011 UDC :519.8 ; :336 Summary Cash s the centre of all fnancal decsons. It s used as bass for the future nvestment projectons and enterprses' fnancal plans. A concept of ntellgent system for fnancal forecastng, the dynamc of ssung nvoces and recevng correspondng cash nflow for far exhbtons s presented n ths artcle. Ths ntellgent system s based on artfcal ntellgent method casebased reasonng (CBR) where the prevous experence for new forecastng s taken nto account. Ths research s a dscusson about the problem of nvoce curve and the correspondng cash nflow curve at the moment of the far exhbton. The nvoce curve reaches ts saturaton pont, whle the cash nflow curve s stll far away from the saturaton pont. The soluton to ths problem s the saturaton pont for the cash nflow curve. Therefore managers want to know how hgh the cash nflow of some servces would at a certan pont of tme n the future, wth respect to nvocng. If they could predct relably enough what would happen n the future, they could plan mportant busness actvtes to ensure faster nvoced ncome and future actvtes. Methodologcal aspects have been tested, n practce, as a part of the management nformaton system development project for Nov Sad Far company. Keywords fnancal forecastng, nvoce, cash nflow, casebased reasonng, decson support 1. Introducton Intellgent nformaton systems use current nformaton to predct the consequence of future acton. One of the prmary roles of these systems s to provde managers wth knowledgebased expertse to make more effectve decsons. In such a way, the systems can provde the support needed to cope wth today s turbulent envronment. Human reasonng s based on the decson makers ablty to process what they know, whether they learn by example or create new approaches. Reasonng s used by managers to dscover data patterns, helps to nfer multple meanngs from a sngle nput, and generalze from dverse nputs. The use of casebased reasonng (CBR) n forecastng the dynamc of ssung nvoces and recevng correspondng cash nflow based on experence stored n the casebase knowledge contaner s presented n ths paper. CBR (Kolodner, 1993) systems make use of past nformaton n order to generate new solutons to new problems. The qualty of the nformaton stored wthn the case base wll determne the qualty of the solutons offered by these systems. Presented research s the prevous project contnuaton n Smć, Kurbalja & Budmac, (2003) and t brngs about sgnfcant enhancements n comparson to the prevous soluton. Performed smulatons have shown that predctons made by proposed CBR fnancal forecastng system dffer only n 10 % n respect to what actually happened. Predctons obtaned n ths manner are mportant for future plannng n a company such as Nov Sad Far, whose core busness s to organze exhbtons, because achevement of sales plans, revenue and company lqudaton are measures of success n corporate busness. The rest of the paper s organzed as follows: The followng secton elaborates, n detal, motvatons and reasons for the ncluson of CBR n ntellgent cash flow forecastng. Secton 3 overvews nvocng and cash nflow pertanng to an exhbton, our prevous research and starts dscusson about our present research. Secton 4 descrbes the predctng model and applcaton of the method to the gven problem by the proposed algorthm. Secton 5 shows measurements carred out and results, whle Secton 6 presents possbltes for mprovng the exstng forecastng system and future work. Secton 7 concludes the paper.
2 Invocng and Fnancal Forecastng of Tme and Amount of Correspondng Cash Inflow 2. Background 2.1. Intellgent Decson Support Requrement Nov Sad Far company s a complex organzaton, and ts basc actvty s organzng trade fars, although t has many other actvtes throughout the year. Ten tmes a year, about 30 far exhbtons are organzed, wth nearly exhbtors takng part, both from the country and from abroad. Desgnng ntellgent decson support based on artfcal ntellgent methods was drven by the results of a survey. The survey was made on the sample of 42 users of the current management nformaton system dvded nto three groups: strategctactcal management (9 people), operatng managers (15 people), and transactonal users (18 people). After very detaled statstcal evaluaton of the survey (Smć, 2004), the followng conclusons, among others, were drawn: Development of the decson support system should be focused on problems closely related to fnancal estmatons and fnancal market trends spannng several years. The poltcal and economc envronment of the country and regon nfluences busness and management, whch means that t s necessary to mplement those nfluences precsely n the observed model. It s also necessary to take nto account future event estmatons. Implementaton of ths nonexactly mathematcal model s a very complex problem. As an example, let us take a look nto the problem accentuated by company managers. Durng any far exhbton, the total cash nflow s only 3050% of the total nvoce amount. Therefore managers want to know how hgh the cash nflow of some servces would be n some future tme, wth respect to nvocng. If they could predct relably enough what would happen n the future, they could plan mportant busness actvtes to ensure faster nvoced ncome and future actvtes whch would lead to a better exhbton. One possble approach to dealng wth external nfluences s observng the case hstores of smlar problems cases over a long perod of tme and makng estmaton accordng to conclusons based on these observatons. Ths approach, generally speakng, ntellgent search whch s appled to solvng new problems by adaptng solutons that worked for smlar problems n the past, s called casebased reasonng. Ths paper also dscusses hgh value cash nflow and forecastng maxmum tme and amount of cash nflow for other busness and fnancal categores The Prncples of Cash Flow Forecastng In general, cash s the centre of all fnancal decsons. It s used as bass for the future nvestment projectons and enterprses fnancal plans. The cash flow forecast dentfes the sources and amount of a busness cash nflow and the destnatons and amount of cash outflow over a gven perod. Cash flow forecastng enables one to predct peaks and troughs n the cash balance. The forecast s usually done annually or quarterly n advance and dvded nto weeks or months. In extremely dffcult cash flow stuatons, a daly cash flow forecast mght be helpful. Cash flows are cash nflows and outflows. A cash flow forecast mght be an nvaluable busness tool f t s used effectvely. One must bear n mnd that t s dynamc one needs to change and adjust t frequently dependng on the busness actvty, payment patterns and suppler demands. Thus a CashFlow Statement may be defned as a summary of recepts and dsbursements of cash for partcular perod of tme. Cash flow Statement traces the varous sources whch brng n cash, for example: cash from operatng actvtes, sale of current and fxed assets, ssue of share captal and debentures, etc., and applcatons whch cause outflow of cash such as operaton loss, purchase of current and fxed assets, redempton of debentures, preference shares and other cash longterm debt. A statement of cash flow, when used n conjuncton wth the rest of the fnancal statements, provdes nformaton that enables users to evaluate the changes n net assets of an entty, ts fnancal structure (ncludng ts lqudty and solvency) and ts ablty to affect the amounts and tmng of cash flow n order to adapt to changng crcumstances and opportuntes. Cash flow nformaton s useful n assessng the ablty of the entty to generate cash and cash equvalents and enables users to develop models to assess and compare the present amount of the future cash flow of dfferent enttes. Hstorcal cash flow nformaton s often used as an ndcator of the amount, tmng and certanty of future cash flow. It s also useful n checkng the accuracy of past assessments of future cash flow and n examnng the relatonshp between proftablty and net cash flow CaseBased Reasonng CaseBased Reasonng (CBR) s a technque that has ts orgn n knowledgebased systems. CBR systems learn from prevous stuatons. The man element of a CBR system s the case base. It s a 15
3 Dragan Smć, Svetlana Smć, Vasa Svrčevć structure that stores problems, elements cases, and ther solutons. So, a case base can be vsualzed as a database that stores a collecton of problems wth some sort of relatonshp to solutons to every new problem, whch gves the system the ablty to generalze n order to solve any new problem. The learnng capabltes of CBR system rely on ther own structures, whch consst of four man phases (Aamodt & Plaza, 1994): retreval, reuse, revson and retan. Fgure 1 shows a graphcal representaton of those four phases. The retreval phase conssts of fndng the cases n the case base that most closely resemble the proposed problem. Once a seres of cases have been extracted from the case base, they must be reused by the system. In the second phase, the selected cases are adapted to ft the current problem. After offerng a soluton to the problem, t s then revsed, to check whether the proposed alternatve s n fact a relable soluton to the problem. If the proposal s confrmed, t s retaned by the system, modfyng some knowledge contaners and could eventually serve as a soluton to future problems. Fgure 1 Basc representaton of the CaseBased Reasonng Cycle (Adapted from Aamodt & Plaza, 1994) The man advantage of ths technque s that t can be appled to almost any doman, even to domans where rules and connectons between parameters are not known. CaseBased Reasonng has been used to solve a great varety of problems. It s a cogntve structure that can be easly appled to solve problems such as those related to soft computng (Lu & Wrtz, 2005), snce the procedures t uses are qute easy to assmlate n the softcomputng approaches. CBR has also helped to create applcatons for a varety of envronments, such as health scences (Corchado, Bajo, & Abraham, 2008), where mages can play an mportant role (Bchndartz & Marlng, 2006), 2006; Herrero, Corchado, Pellcer, & Abraham, 2009) or elearnng (Decker, Rech, Altho, Klotz, & Voss, 2005). As t has evolved, CBR has been used to solve new problems, appled as a methodology to create plans, and broken down nto dstrbuted verson (Peranez & Pascual Granged, 2008). Oceanographc problems (Fdez Rverola & Corchado, 2004) have also been addressed usng these technques n order to predct the value of hghly nconsstent parameters. The use of past knowledge to generate new solutons makes CBR systems very useful as a decson support system. Dstrbuted and multagent (Carrascosa, Bajo, Julan, Corchado, & Bott, 2008) systems have used the CBR methodology to explot ts decsonsupport capabltes as an addton to ther characterstcs. On the other hand, as t s a methodology, CBR has been successfully appled to qute dfferent knowledge felds and combned wth a great varety of technques. Most of the technques used wthn CBR systems serve to classfy, adapt, revse solutons, etc. Artfcal neural networks and fuzzy logc have also been used to complement the capabltes of the CBR methodology (Hsu & Ho, 2004). Another way of usng neural networks to adapt the retreved nformaton s to change the weght of the connecton between the neurons dependng of the retreved cases (Zhang, Ha, Wang, & L, 2004). Changng the weghts allows the system to adapt the soluton to the problem. If the casebase structure s ntegrated nto neural network, then the revson phase conssts of changng the organzaton of the case base, dependng on the correctons of the proposed result and other neural varables such as neuron age, actvaton value and last use (Watson, 1999). The man problem wth CBR s to fnd a sutable measure of smlarty the measure that can tell to what extent two problems are smlar. The hgher the values of the smlarty functon are, the more smlar are the objects. Smlarty measures, such as the knn (k nearest neghbors) and also modern varatons such as Sgnfcant Nearest Neghbor where the value of k, whch s the number of neghbors to consder, s calculated by takng nto account the dssmlarty between the new case and the past cases stored n the case base, have been used to retreve cases from the case base. Genetc algorthms (GA) are also used to revse the correctons and the solutons. After runnng those algorthms, the solutons can be accepted, and added to the case base. 16
4 Invocng and Fnancal Forecastng of Tme and Amount of Correspondng Cash Inflow 3. Invocng and Cash Inflow Pertanng to an Exhbton For the last 78 years, the far exhbtons have been drawng attenton of both the wde audence and the experts. They started at Nov Sad Far as exhbtons of agrcultural products, mechanzaton, food and lvestock dsplays. Today, the Internatonal Far of Agrculture successfully presents 1800 domestc and 200 foregn companes, whch are the most dstngushed representatves from all felds of agrcultural producton and foodprocessng ndustry n South Eastern Europe. The company, Nov Sad Far, possesses data on nvocng and payment processes for every exhbton durng the perod of 5 years  contanng between 27 and 32 exhbtons per year. The processes are presented as sets of ponts where every pont s determned by the tme of measurng (day from the begnnng of the nvocng process) and by the amount beng nvoced or receved on that day. It can be concluded that these processes can be represented as curves (Fgure 2). Fgure 2 Invocng, cash nflow (payment), uncharged curves and the mportant ponts for a far exhbton (Adapted from Aamodt & Plaza, 1994) By analysng these processes, the process of nvocng usually starts several months before the exhbton (the frst mportant pont) and the amount beng nvoced grows rapdly untl crca the exhbton begnnng. After that tme, the amount beng nvoced remans approxmately the same tll the end of the process. The process of payment starts several days after the correspondng process of nvocng (the second mportant pont). After that, the amount of payments grows, but not as quckly as the amount beng nvoced. Then comes the pont when the amount of unsettled nvoces becomes the largest (the thrd mportant pont), and t s presented on the unsettled nvoces curve. At the moment of an exhbton (the forth mportant pont) the amount of payment s most frequently between 25% and 65% of the amount nvoced. Afterwards, the amount of payment contnues to grow untl t reaches a constant value and stays approxmately constant tll the end of the process. The saturaton tme of payments s usually a couple of months later than the saturaton tme of nvocng, and the payment saturaton amount s always less than or equal to the saturaton amount of nvocng. A good busness result s consdered to be when 90% of the nvoced amount has been settled or, conversely, 10% has remaned unsettled (the ffth mportant pont). The analyss shows that the saturaton amount of payments s between 80% and 100% of the saturaton amount of nvocng. The maxmum amount of payments represents the amount of payment acheved by regular means. The balance s expected to be settled later by court order, other specal busness agreements or, perhaps, wll reman unsettled due to debtor bankruptcy Prevous Research In prevous research, every nvoce and all cash nflow curves, respectvely, conssted of 100 ponts of nvoce (cash nflow) value measured every 4 days out of 400 days from the begnnng of nvocng. The ponts were connected wth cubc splne, as smooth curves. Invoce and cash nflow curves had been put n the same startng pont, whch smplfed smlarty measures among the curves, but t dd not succeed completely (Smć et al., 2005). The prevous research contans dscussons about the problem of nvoce curve and the cash nflow curve at the moment of the exhbton. Then, the nvoce curve reaches ts saturaton pont, whle the cash nflow curve s stll far away from the saturaton pont. The soluton to ths problem s the saturaton pont for the cash nflow curve Present Research The presented research s the contnuaton of the prevous project ( ) (Smć, Kurbalja, & Budmac, 2003; Smć, Kurbalja, & Budmac, 2004; Smć, Kurbalja, Budmac, & Ivanovć, 2005; Smć & Smć, 2007). As t was mentoned n Smć et al. (2005), several sgnfcant changes have been made for the future,.e. present research. Invoce and cash nflow curves start at dfferent tmes representng a realstc busnessmakng manner. Thus, there s a tme dfference between the frst nvoce moment and the frst cash nflow moment. Secondly, nvoce and cash nflow are measured every day durng perod of 400 days. Thrdly, nstead of cubc splne nterpolaton, lnear 17
5 Dragan Smć, Svetlana Smć, Vasa Svrčevć nterpolaton was appled among every sngle pont. Ths has brought about the usage of a new algorthm for the lnear nterpolaton case. It can be consdered that prevously started CBR system adjustments ft the realstc busnessmakng manner to a great extent. As an example of ths exhbton busness actvtes are presented for the Internatonal Far of Agrculture n Table 1 for the perod from 2000 to It s very mportant to menton that ths exhbton s the bggest money maker n Nov Sad Far Company. Table 1 Internatonal Far of Agrculture  Basc data about exhbton per year Year The begnnng of nvocng The begnnng of payment Payment delay (days) The duraton of exhbton % of payment at the begnnng of exhbton % of payment n 400 days tme The nvocng of the most mportant exhbton, takng place n May, whch usually starts n December of the prevous year, and cash nflow of servces, whch s mostly performed by the end of a calendar year, represents the process that lasts for about 400 days. As t can be seen, an average cash nflow delay s 12 days from the day of nvocng. It s very mportant to decde whch day wll be the frst day of nvocng because of the followng events: Chrstmas Invocng and Fnancal Forecastng of Tme and Amount of Correspondng Cash Inflows (25/12); the end of the tax and busness year (31/12), New Year (01/01), Orthodox Chrstmas (07/01), and Orthodox New Year (14/01). In these days the busness actvty s reduced and companes are very careful about makng payments. It s also very mportant to menton that the Far Company pays salares n that perod. The percentage of unsettled nvoces at the begnnng of the exhbton s form 39 to 65%, whch s 9599% of the total nvoced amount for 400 days from the begnnng of the nvocng process representng very good fnancal result. 4. Model and Applcaton of the System 4.1. Predctng Model As mentoned before, ths research s a dscusson about the problem of nvoce curve and the cash nflow curve at the moment of the exhbton. The nvoce curve reaches ts saturaton pont, whle the cash nflow curve s stll far away from the saturaton pont. The soluton to ths problem s the saturaton pont for the cash nflow curve. The system therefore analyss curves, n the followng way: past nvoce and cash nflow curves are stored n casebase; casebase, also, ncludes relevant data about exhbtons; preprocessng data from casebase; compare new case to smlar curves from the past, stored n casebase retreve process; extract the best fttng nvoce and cash nflow curves retreve process; adapt extracted curves accordng to prevously defned rules reuse process; automated numercal and logcal control adapted curves revse process; predct the future behavor of the cash nflow curve revse process; and predct amount and tme of saturaton pont on the cash nflow curve revse process. Retan actvty s not mplemented n ths model because human must confrm new case, and add new case, modfyng some knowledge n casebase The Calculaton Cash Inflow Saturaton Pont The cash nflow saturaton pont for one predcton s calculated usng ten most smlar nvoce curves and ten most smlar cash nflow curves from the retreved cases from casebase. Snce the values of saturaton pont are dfferent for exhbton, every curve from the casebase must be scaled wth a partcular factor. In such a way, the nvoce values of saturaton of the old curve and actual, newcase, are the same. On the other sde, the cash nflow values of saturaton of the old curve and actual, newcase, also are the same. That factor s calculated as: Factor actual _ value _ of _ saturaton (1) old _ value _ of _ saturaton The factors are calculated for nvoce saturaton pont and correspondng cash flow pont receptvty, for every curve. The system calculates dstance. The dstance between two curves can be represented as a surface between these curves. When dstance s known, then the smlarty (sm) can be effcently and easly calculated as: 18
6 Invocng and Fnancal Forecastng of Tme and Amount of Correspondng Cash Inflow 1 sm 1 dst (2) Table 3 Internatonal Far of Electroncs 2004 Measurement results for 10 most smlar The values of goodness are drectly proportonal to the smlarty between old and actual curves, but the sum of all goodness must be 1. The goodness for every old payment curve s calculated as: goodness sm all _ sm At ths moment the adaptaton soluton of cash nflow saturaton pont s calculated as: (3) sat _ pon goodness sat _ pon (4) Now, retreve, reuse and revse CBR processes are fnshed and forecastng for saturaton pont for new case of cash nflow curve s done. 5. Measurement and Expermental Results The casebase ncludes 142 cases for the exhbtons between from 2000 to The measurements of already known nvoce and cash nflow value sets are done. Forecastng for past far exhbtons has been made n order to provde accuracy of the proposed fnancal forecastng support system for assessng the values and tmes of regular cash nflow. The results of several conducted measurements for a number of past far exhbtons wll be shown n the followng text. Measurements of the largest and the worthest far exhbton wll be presented for: the Internatonal Far of Agrculture n 2001 (Table 2) as well as the Internatonal Far of Electroncs n 2004 (Table 3). Table 2 Internatonal Far of Agrculture 2001 Measurement results for 10 most smlar As t has already been stated the results of these measurements and casebased reasonng forecastng system show good results wth the predcton error for: the amount of regular debt payment from 5 % to 10 % the tme span of regular cash nflow from 2 % to 10 %. Other measurements have also been completed showng that when the proposed forecastng system s used wth a set of already known values, fnancal forecastng support system based on CBR method get results whch n average dffer up to 10 % n value of what actually happened n the past. These results are qute better than n our prevous research (Smć & Smć, 2007). 6. Improvng the Exstng Forecastng System and Future Work Although acheved results of ths fnancal forecastng and decson support system gve sgnfcantly good outcome, the research of the project can be contnued. There are several mportant ssues that research could focus on n the future. 1. Small mprovement of the proposed forecastng algorthm As measurement results show, there s qute dfferent tme span between forecastng and the real tme saturaton pont. It could be mproved f the forecastng system s programmed to take nto account behavor of the cash flow curves from casebase and after the nvoce saturaton pont. 2. Soluton, revson and retanng The system, too, has to support revson of the soluton and the retanng of the soluton fulfllng 19
7 Dragan Smć, Svetlana Smć, Vasa Svrčevć the basc casebased reasonng model. By memorzng: the new case (the problem); suggested soluton; the number of smlar curves for obtanng suggeston; the real soluton; the forecastng system uses the nformaton n the phase of reuse for the soluton of the future problems. 3. Fnancal forecastng algorthm mprovement The exstng algorthm operates wth predctons at the moment of completng the exhbton servce nvocng process. A new algorthm could provde predctons durng the whole perod of nvocng and cash nflow tme (perod). Ths means that cash nflow forecastng can be done mmedately after the frst nvoce servce cash nflow. Also, when the nvocng reaches saturaton pont and cash nflow contnues to grow towards saturaton, forecastng should gve better results snce the cash flow system converges the fnal value of the amount and tme of cash nflow. 7. Concluson Ths paper descrbes, n great detal, the casebased reasonng part of the system, gvng a thorough explanaton of one case study. One secton of the paper presents the ntellgent decson support requrements, the prncples of cash flow forecastng and the bass of artfcal ntellgent method casebased reasonng. In the other secton of the paper, nvocng and cash nflow pertanng to a far exhbton s presented. In the thrd secton, proposed model, algorthm based on CBR method, applcaton of the cash nflow forecastng system and the measurement and expermental results are presented. The measurement and expermental results have been completed showng that, when the proposed forecastng system s used wth a set of already known values, fnancal forecastng system based on CBR method gets results whch n average dffer up to 10 % n value of what actually happened n the past. Although acheved results of ths fnancal forecastng gves sgnfcantly good outcome, the future research of the project can nclude the followng: (1) small mprovement of the proposed forecastng algorthm; (2) upgrade of the system for all CBR phases retreve, reuse, revse and retan; and (3) new fnancal forecastng algorthm whch provdes predcton durng whole perod of nvocng and cash nflow. The presented system s not lmted to ths case study but t can be appled to other companes values as well as expenses, nvestment and proft. References Aamodt, A., & Plaza, E. (1994). CaseBased Reasonng: Foundatonal Issues, Methodologcal Varatons, and System Approaches. AI Communcatons, 7 (1), Bchndartz, I., & Marlng, C. (2006). Casebased reasonng n the health scences: What's next? Artfcal Intellgence n Medcne, 36 (2), Carrascosa, C., Bajo, J., Julan, V., Corchado, J. M., & Bott, V. (2008). Hybrd multagent archtecture as a realtme problemsolvng model. Expert Systems Wth Applcatons, 34 (1), Corchado, J. M., Bajo, J., & Abraham, A. (2008). GERAmI: Improvng the delvery of health care. IEEE Intellgent Systems. Specal Issue on Ambent Intellgence, 3 (2), Decker, B., Rech, J., Altho, K. D., Klotz, A., & Voss, A. (2005). epartcpatve Process Learnng: processorented experence management and conflct solvng. Data Knowledge Engneerng, 52 (1), FdezRverola, F., & Corchado, J. M. (2004). FSfRT: Forecastng System for Red Tdes. Appled Intellgence, 21 (3), Herrero, A., Corchado, E., Pellcer, M. A., & Abraham, A. (2009). MOVIH IDS: A MobleVsualzaton Hybrd Intruson Detecton System. Neurocomputng, 72 (1315), Hsu, C. C., & Ho, C. S. (2004). A New Hybrd CaseBased Archtecture for Medcal Dagnoss. Informaton Scences, 166 (14), Kolodner, J. L. (1993). Casebased Reasonng. San Mateo: Morgan Kaufmann Publshers. Lu, X., & Wrtz, K. W. (2005). Sequental Negotaton n Multagent Systems for Ol Spll Response DecsonMakng. Marne Polluton Bulletn, 50 (4), Peranez, R., & PascualGranged, A. (2008). Modellng surface radoactve, chemcal and ol splls n the Strat of Gbraltar. Computers and Geoscences, 34 (2), Smć, D. (2004). Fnancal predcton and decson support system based on artfcal ntellgence. Unpublshed doctoral dssertaton, Unversty of Nov Sad, Nov Sad. Smć, D., & Smć, S. (2007). An approach to effcent busness ntellgent system for fnancal predcton. Soft Computng, 11 (12), Smć, D., Kurbalja, V., & Budmac, Z. (2003). An applcaton of casebased reasonng n multdmensonal database archtecture. Proceedngs of 5th Internatonal Conference on Data Warehouse and Knowledge Dscovery (DaWaK), Lecture Notes n Computer Scence (pp ). Hedelberg: Sprnge. Smć, D., Kurbalja, V., & Budmac, Z. (2004). Improvng the CBR system for fnancal predcton. Proceedngs of the Internatonal Conference on Informaton and Knowledge Engneerng (IKE) (pp ). Las Vegas: CSREA Press. Smć, D., Kurbalja, V., Budmac, Z., & Ivanovć, M. (2005). Casebased reasonng for fnancal predcton. Proceedngs of 18th Internatonal Conference on Industral and Engneerng Applcaton of Artfcal Intellgence and Expert Systems (IEA/AIE). Lecture Notes n Computer Scences (pp ). Hedelberg: Sprnger. Watson, I. (1999). Casebased reasonng s a methodology not a technology. KnowledgeBased Systems, 12 (56), Zhang, F., Ha, M. H., Wang, X. Z., & L, X. H. (2004). Case adaptaton usng estmators of neural network. Proceedngs of 2004 Internatonal Conference on Machne Learnng and Cybernetcs (pp ). IEEE: Pscataway. 20
8 Invocng and Fnancal Forecastng of Tme and Amount of Correspondng Cash Inflow Dragan Smć Unversty of Nov Sad Faculty of Techncal Scences Trg Dosteja Obradovća Nov Sad Serba Emal: Svetlana Smć Unversty of Nov Sad Faculty of Medcne Hajduk Veljkova Nov Sad Serba Emal: Vasa Svrčevć Sunoko Trg Marje Trandafl Nov Sad Serba Emal: 21
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