An Architecture for Intelligent Assistance in the

Proceedingsof the 28th Annual Hawaii Intemational Conferenceon SystemSciences- 1995

An Architecture for Intelligent Assistancein the Forecasting Process G. Mentzas, I. Linardopoulos, V. Assimakopoulos Department of Electrical and Computer Engineering National Technical University of Athens

Abstract The need to effectively integrate decision-making tasks together with knowledge representation and inference procedures has caused recent research efforts towards the integration of decision support systems with knowledge-based techniques. In the present paper we explore the potential benefits of such an integration in the area of statistical forecasting. In an attempt to clarify the elements of the forecasting process which could be enhanced by intelligent assistance, a generic forecasting process is described and its main functional elements are identified. lIthe paper describes the architecture of such a system (Intelligent Forecasting Information System, ZFZS), in which, besides the traditional components of a decision-support system four constituents are included that try to model the expertise required: a Z+ocess Expert, a Learning Expert, a Data Expert and a Model Expert.

enhancing the mission-critical elements of corporate decision-making; see e.g. Mentzas (1994b) for an illustration of these features in the production management environment. Business forecasting seems to be an area in which such an integration of decision support with intelligent features could generate critical advantages for corporate users; see e.g. Collopy and Armstrong (1992) and Assimakopoulos and Konida (1992). The latter authors, based on the fact that the forecasting process is a complex decision-making task, in which the manager can benefit significantly from assistance in the identification of available data and scientific resources, as well as from access to domain knowledge and forecasting expertise, argue that forecasting constitutes an area rich in potential for the development of knowledge-based techniques. On the other hand, it has been argued that while the forecasting practitioner is reliably assisted by existing software in the selection of appropriate model specifications, there remain a number of topics where he/she is left largely to his/her own devices, expertise and judgement; see e.g. Tashmau and Leach (1991). Some of these topics refer to: l the data comparisons, transformations and screening procedures for spotting outliers and discontinuities during preliminary data analysis; l the consideration of multiple methods and different fit criteria in the method selection process; l the attention to crucial method evaluation issues, such as measurement of bias in the forecasts and postsample simulations; l the need to apply several forecasting methods in parallel and/or in combination; etc. From the above considerations, it can be deduced that the work of forecasters would be greatly enhanced if the computer-based systems available to them were able to provide intelligent assistance in: l establishing a systematic and methodical approach to the forecasting process; l understanding the properties of alternative methods and the implications of their selection on forecasting accuracy;

Introduction The need to effectively integrate decision-making tasks (such as interactive access to data and support for numerical and quantitative modelling techniques in semistructured problems), together with the knowledge representation tasks and inference procedures that model an expert’s thought process, has provoked recent research efforts to integrate decision support systems (DSS) with knowledge-based expert systems. Various forms of this integration have been examined and a variety of systems architecture have been proposed; see e.g. Turban and Watkins (1986), Rockart and DeLong (1988), Gottinger and Weimann (1992), Edwards (1992), Turban (1990) and ElNajdawi and Stylianou (1993). Moreover, based on a broad classification of current types of information systems, Mentzas (1994a) identified some of the essential features for intelligent decision-making support. Among them, issues related to interpretation, reasoning, and learning seem to be the most crucial for

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Proceedings of the 28th Hawaii International Conference on System Sciences (HICSS '95) 1060-3425/95 $10.00 © 1995 IEEE

Proceedingsof the 28th Annual Hawaii international Conferenceon SystemSciences- 1995 l comprehending the effects of past actions/events and their consequences on causing changes on established patterns or relationships; l judgmentally adjusting the forecasting results, by adopting learning strategies and embodying knowledge from historical experience and similar cases. The present paper presents an architecture for a computer-based system that attempts to cover the above requirements, i.e. an Intelligent Forecasting Information System (IFIS). Besides the traditional components of a decision-support system (i.e. database, model base and dialogue management systems) the architecture includes four elements that try to model the expertise required: a Process Expert, a Learning Expert, a Data Expert and a Model Expert. The paper is structured as follows. The next section gives a description of a model of the forecasting process, in which the most significant steps of the process, and the tasks carried out in these steps are identified. Such a model provides the background for defining the expertise required by the computer-based system. The third section gives a description of the components of the architecture, an overview of the functioning of the system. It also illustrates two major aspects of the proposed architecture : the object the representation and knowledge oriented intercommunication between the expert components. The final section outlines the conclusions of the present work and gives directions for further research.

A model of the forecasting

particular features and structure of the problem at hand. In this sense we concluded to five major steps the experienced practitioner takes while going through forecasting process. They are the following: a. Definition of objectives and restrictions b. Data preparation C. Diagnostic checks d. Method selection e. Judgmental adjustments A brief description of these steps is given in the paragraphs below.

Definition of objectives and restrictions A number of preliminary specifications are usually considered before any further decision is made, concerning the forecasting strategy to be followed. We summarise the most important of these specifications in the form of “What is the.. . ” questions accompanied by relevant remarks: l Nature of items to be forecasted. The nature of the time series may be described as macroeconomic, etc. business, demographic, and/or monthly, quarterly, etc. Special case-studies, focusing on some of these categories, have come up with useful conclusions about the appropriate forecasting methodology to be used in each category. l Number of items to be forecasted. For example, if the practitioner wishes to produce forecasts for a large number of items, he/she may employ a time series model (e.g. smoothing) just because it is more simple and quite reliable. l Level of aggregation. In some cases, it is useful to aggregate the items to be forecasted into several groups that share common traits (e.g. products with low demand). This may yield more accurate forecasts, while saving computational time. l Forecasting horizon. Method selection is partly based on the desired forecasting horizon, since it is known that some methods are completely inappropriate for long or intermediate horizons (e.g. Holt’s exponential smoothing). l Nature of the available historical information. This may be qualitative (knowledge of causal forces or particular events that have significantly influenced past values of the forecasting variable) and/or quantitative (historical data). An experienced practitioner must select the forecasting method that fully utilises the available information. l Number and nature of independent variables. The use of independent variables other than time, makes

process

Many authors and scientists have implicitly or explicitly depicted the forecasting process as a sequence of steps the practitioner has to take in order to reach a satisfactory prediction of some future value [Godrich (1989)]. In this section we attempt to describe in a more systematic way these steps and the corresponding tasks, believing that a detailed definition of the forecasting process provides a sound starting point for further research in the way the latter could be assisted by knowledge-based techniques. The effort to describe any “process” employed by humans to solve a problem (in our case forecasting) due to the difficulties encounters may interdependence among the tasks involved in it, as well as the capacity of human mind to simultaneously evaluate all the available information and schedule an appropriate set of actions. Hence, focus is usually placed on the identification and categorisation of the relevant tasks, rather than the specification of their exact order which may slightly vary according to the 168


Proceedingsof the 28th Annual Hawaii I~temational Conferenceon SystemSciences- 1995 regression models a possible solution to the method selection problem.

l Calculation of the values of the model’s parameters that minimise the loss function for the desired period of the historic data; and l Running of each model over the historic data. Model evaluuhn comprises of the following tasks: l Selection of one or more criteria to estimate the direction (bias) and magnitude (accuracy) of the forecasting errors both within and out-of sample; l Analysis of the residuals’ original plot and correlogram to detect outliers, the need of transformation in the original series or an underlying pattern “neglected” by the model; l Examination of comparative summary tables and graphs ; l Choosing of the model that minimises error measures and produces uncorrelated, stationary, normally distributed residuals, and use of the model to extrapolate from the last historical point.

Data preparation At this stage, the practitioner usually performs the following tasks: l Retrieval aud plotting of appropriate data files l Checks for missing or extreme values l Decisions whether certain values represent outliers or simply input errors l Appropriate modifications, corrections, etc.

Diagnostic checks The exploration of data via summary statistics and graphs, highly recommended in the forecasting literature, is a prerequisite to the good forecasting practice, as it allows for the detection of the specific features of the time series: trend, seasonality, cyclical components, degree of irregularity and stability of

Judgmental adjustments The forecasts produced by the previous step must be judgmentally adjusted according to contextual knowledge concerning future events or changes in the direction of causal forces, as these may affect the future values of the time series. To do this the practitioner must: l identify the nature of the oncoming events l detect similar historical events l quantify the effects of each one l calculate their simultaneous effect and combine it with the statistical forecast.

Vti6tlCe.

Practically, one has to go through the following tasks: l Plotting of original data to get a rough idea of the series major features (trend, seasonality, stationarity and irregular component); l Calculation of autocorrelation functions and plotting of correlogram to determine whether or not the series is stationary; l In case it is not stationary, the practitioner may explore the possibility to turn it into a stationary one. He/she may, for example, use the Box-Cox power transformation [Goodrich (1989)] to obtain stationarity in the variance, or differencing to obtain a stationary mean.

Implications

for Intelliient

Assistance

The preceding description of the forecasting process indicates the way one could follow to reach the ultimate goal, i.e. the utilisation of all available information in order to produce reliable forecasts. The steps of the process represent simple stages during which the “expert” practitioner manipulates different subsets of the available information and draws conclusions, which may be used as input for the next step. Assume, for example, that in the first step the “expert” practitioner processed the following information: “the forecasting horizon is long” and “the number of time series points is small”, then he/she would probably infer that “the Box-Jenkins method is inappropriate for the specific problem “. This conclusion corresponds to a “higher level” of information and could serve as input to the third and fourth steps of the process (i.e. model selection and judgmental adjustments).

Method selection At this stage the practitioner utilises the information provided by the first and third step to specify the family of the forecasting methods that is more compatible with the problem’s structure and seems to best fit the time series data. Next, he/she usually identifies a particular member or several candidates of the family that best matches the available data. To do this, one must first fit each model to the historic data and then evaluate its performance. Model fitting usually comprises of the following tasks: l Selection of a fit criterion (a “loss function” and a period of fit);

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Proceedingsof the 28th Annual Hawaii Inremational ConferenceORSystemSciences- 1995 It is clear that going through the stages, the practitioner progressively organises the available information into higher level “knowledge chunks” by using the appropriate conceptual tools and heuristics. An overview with some example of these tools and the corresponding sources of information for each step are given in Table 1. The problem would then be to set up an architecture that would provide intelligent support for the forecasting process, by explicitly covering the knowledge representation and processing tasks that are included in the above mentioned steps of the process.

Structure

makes use of the potential of knowledge-based techniques. The knowledge-based part of the system incorporates knowledge about “procedures” rather than domain-specific knowledge. Besides the traditional components of a decisionsupport system (i.e. Database management system (DBMS), model base management system (MBMS) and dialogue management system (User Interface)) the proposed architecture includes four components (Figure 1): the Process, Data, Model and Learning Experts. In the same figure we may also see the Data and Model bases, where the quantitative data and the model algorithms are stored In the heart of the architecture there is the Process Expert which represents the whole problem and controls the other Expert modules that provide expertise in specific tasks. In other words, the Process Expert is the omniscient component about the state of the whole system and contains the problem solving scheme. .

of the architecture

An Overview Based on the forecasting process described in the previous section, we propose an architecture for an Intelligent Forecasting Iuformation System (IFIS) that ShP 1. Definition of objectives and restrictions 2. Data preparation

sources of information Preliminary specifications Outcome of 1st step

3.

Diagnostic checks

Outcome of 1st step

4.

Method selection

Outcome of 1st and 3rd steps

5.

Judgmental adjustments

Outcome of 1st step

Table 1. Sources

of information

and processing

Conceptual tools and heuristics

.

Simple rules

. Access on data files . Algorithms for aggregation . Algorithms for the treatment of outliers . Access on data files 0 Calculation of autocorrelation functions . Algorithms for stationarity tests . Algorithms for power transformations . Sophisticated rules for the selection of fit criteria . Algorithms for the optimisation of method parameters . Algorithms for calculating residuals for autocorrelation . Access on comparative summary records . Post sample simulation techniques . Access on historic records . Algorithms for the identification of similar cases and the quantification of the effect of various events tools used in the steps of the forecasting

170 Proceedings of the 28th Hawaii International Conference on System Sciences (HICSS '95) 1060-3425/95 $10.00 © 1995 IEEE

process

Proceedings of the 28th Annual Hawaii International Conference on System Sciences -

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MODEL EXPERT 4

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PROCESS EXPERT

The Data Expert

The Model

The Learning

Expert

Expert

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The Process Expert

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DATA EXPERT 9

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a) represents the whole problem (maintains the problem solving scheme) b) controls the other components a) enhances the function of the DBMS by providing a powerful searching mechanism b) performs various operations on the data which are essential to the forecasting process, such as calculation of acfs, differentiation and power transformation. a) controls the MBMS b) selects fit creteria for the optimisation of model parameters c) computes various error measures a) provides expertise in method selection b) provides judgmental adjustment techniques

Table 2. The functions

of the expert

components

The basic steps that incrementally lead to the solution of the problem at hand may be thought as a predefined algorithmic process, “stored” in the Process Expert’s Inference engine. The latter issues commands to the other three components that execute the appropriate actions and report the results. Of course, a number of different approaches could be equally effective for a specific problem, or inversely, a particular problem may call for special treatment. This implies the fact that the algorithmic process is not necessarily unique. Nevertheless, any variation should be consistent with the forecasting process steps that were previously described.

The Data expert has direct access to the DBMS and enhances its function by providing a n interactive performing several mechanism and searching operations on the data such as differentiation, mathematical transformation, etc. The Model expert controls the MBMS through which it accesses the routines of the forecasting models, executes them and computes forecasts after selecting a suitable fit criterion. Finally, the Learning expert has a twofold role as it contributes in both the method selection and the judgmental adjustment of the statistical forecasts. Table 2 summarises the Expert components functions.

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Proceedingsof the 28th Annual Hawaii International Conferenceon SystemSciences- 1995 3. The third step is quite similar with the expertise provided by some of the existing automatic forecasting software and it entails diagnostic checks upon which the system evaluates the possibility for the time series to achieve stationarity and hence, the applicability of Box-Jenkins methods. Additionally, the system explores the major qualitative features of the series (seasonality and trend) in order to fit the appropriate Exponential Smoothing and Naive models. The reasoning employed by IFIS during this stage is given in the next four paragraphs. a) The system NDS Naive1 or Naive2 for nonseasonal or seasonal data respectively. To detect seasonal patterns, the system computes the autocorrelation functions (acf) of both the original series and the first differences, as often the nature of a seasonal pattern emerges more clearly in the acf of the differenced series [Pankratz 19831. Beginning with naive methods is perfectly consistent with the concept that any other more sophisticated method has to perform substantially better in order to be preferred. b) Preliminary tests concerning the variance and the autocornelation structure of the initial series are required in order to proceed in the evaluation of more sophisticated methods such as ARIMA and Exponential Smoothing models. The autocorrelation structure, related to the possibility for a time series to achieve a stationary mean, can be assumed “strong”, “weak”, or “unclear” depending on the lap at which the ads converge to zero. c) Then, the system evaluates only Exponential Smoothin in the occurrence of the followin cases:

The problem solving scheme With this in mind, we propose the following problem solving scheme (algorithm), adopted by IFIS, and consisting of seven major steps: 1. Inquire the user about preliminary specifications 2. Retrieve and prepare the time-series 3. Check for stationary variance - Transform if necessary l Identify seasonality (through the calculation of ad)- Differentiate if necessary l Check for stationary mean (through the calculation of acf) - Differentiate if necessary l Perform Naive1 or Naive2 for seasonal patterns l 1’ the series can be transformed into a stationary one * perform Exponential smoothing and BoxJenkins else * perform Exponential smoothing l 1f the user requests the use of explanatory variables 3 perform Dynamic Regression 4. Use rule-based logic to combine different methods 5. Evaluate the methods out-of-sample performance (over a short testing period) and choose the one with the minimum MSE or MAPE and those whose MSE or MAPE is not higher than 1% 6. Use historic (quantitative) information to select one from the above methods (process-learning mechanism) (qualitative) information to 7. Use historic judgmentally adjust statistical forecasts (event-learning mechanism)

~1

ln the following paragraphs we give a more detailed description of the above steps without explicitly refer to the intercommunication between the expert modules

The above rule is supported by empirical evidence concerning the majority of economic and business time series, a field to which IFIS is mainly oriented. On the contrary, the system focuses exclusively on the ARIMA models only in cases that the autocorrelation structure is strong and the variance is stationary. Any other combination of these two parameters will result in the evaluation of both Exponential Smoothing and ARIMA methods. d) Dynamic Regression is considered if the user suggests the use of explanatory variables. Diagnostic tests concerning the correlation between the variables (dependent or independent) and the statistical significance of the corresponding coefficients are automatically performed and the user is informed through summary tables about the performance of the

the user is required to In the beginning 1. answer a set of preliminary questions concerning his general objectives, the type of information available, his forecasting experience and skills etc. Then the system processes these specifications and influences the functioning of the entire system, in order to best fit the users needs and the problem at hand. Next, the system retrieves or aggregates the 2. appropriate data files, checks for missing or extreme values and interacts with the user to identify whether they represent data entry errors or outliers. In this case, the system replaces the existing value with a smoothed one.


Proceedingsof the 28th Annual Hawuii International Conferenceon System Sciences- 1995 events, such as strikes, technologhal Of methodological upgrades, changes in the competitive environment ox the governmental price policy . They consist of three fields the first of which refers to the name and nature of the time series, while the second to the event’s type, intensity and the date it occurred. These are specified by the user. Once this is done, the system automatically updates the third field containing inforplation about the corresponding impact. To quantify the impact of a particular evat the system follows a two-steps procedure. First, an appropriate function is selected according to the evat’s type. Then the system retrieves the actual value(s) pertinent to the date(s) the event occurred. The actual values are used as input variables to these functions. Whenever the user requests to judgmentally adjust statistical forecasts in view of some oncoming event(s), the system identifies similar or identical historical case(s) stored in the database. If an identical historical case exists, the program will return the previously observed impact. For similar case(s) both the name of the time series or the intensity of the event(s) may vary. The system performs dynamic regression between the impact (depended variable) and the intensity (explanatory) variables.

method and the effect each variable has on the forecasting equation. 4. In the fourth step, the system employs a rulebased logic to combine several methods (Random walk, smoothing, Brown Holt Exponential Exponential smoothing and Linear regression). Environmental factors and the time series qualitative features are taken into consideration before forecasts for the long and short term model are calculated for each method separately. The final results are produced in the form of a weighted average (Collopy aud Armstrong). 5. For the evaluation of the methods reviewed by the system the Model Expert performs rolling simulation, a technique adopted by some of the existing software packages in the realm of forecasting, and reaches a set of few recommendations. A sample of data points equal to the length of the forecasting horizon (h) is withheld from the end of time series (n), the rest of which serves as the fitting period. Both USE and MAPE are computed for each forecasting horizon and method while the origin of the forecasts shifts forward from the n-h to the n-2 data point. It is important to noti@e that the model coefficients are not recalculated for each new origin. The method with the minimum MSE or MAPE and those whose MSE or MAPE is not higher than 1% , are qualified for further investigation. 6. The system’s intelligent dimension is mainly represented by the last two steps. ln the sixth, called the process-learning mechanism, the system utilises quantitative information to select one from the qualified methods. For each time series historic records, containing actual values and the forecasts each method produced in the past, are stored in the Database. This allows for the estimation of an error distribution for each method and forecasting horizon, throughout the history of the particular time series. The user may indicate the extend to which past contains useful information about the currently established pattern. Then the system calculates error measures (for each method and forecasting horizon) throughout the designated time span and indicates the “winner” method. This technique can be thought as an enhancement to the idea of sliding simulation, as i) the testing period varies according to the user’s wishes and ii) the model coefficients are reestimated while the origin of forecasts shifts forward. In the seventh step, called the event-learning 7. is used to mechanism, qualitative information judgmentaly adjust statistical forecasts. In the proposed architecture special files are stored in the about various Database, containing information

Attributes of the expert components For the implementation of IFIS architecture we must define the attributes for each expert component, that is to say their knowledge and capability requirements . It is more than obvious that the Process expert has a prominent role in the system’s organisation and thus it must possess abilities such as the ones of reasoning, perceiving, communicating and acting. The other expert components should be limited reasoners and perceive only a part of the entire problem, but they must as well communicate and act concurrently with each other. Consequently, the requirements for each one, are separately considered and presented in the following points. The Process Expert must : Communicate with the user and adapt to 1. his/her needs Know what tasks the other components can 2. execute 3. Be able to communicate commands to them Have a reasoning mechanism to organise the 4. information provided by the other expert components “knowledge chunks” that into higher level incrementally lead to the solution of the problem

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Proceedings of the 28th Hawaii International Conference on System Sciences (HICSS '95) - . -. _. 1060-3425/95 ____.____(__._ $10.00 _-----.© 1995 .- -- IEEE

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Proceedingsof the 28th Annual Hawaii

Intemational

Conferenceon SystemSciences- 1995 We believe that Object oriented design (OOD) techniques are the indicated method for the representation of the basic elements involved in the forecasting process. They provide a flexible and structured way to orgauise these elements into objects characterised by properties and belonging to classes (or subclasses). Reasoning can be easily performed on such a static representation, which is described in the following paragraphs.

5. Maintain the problem-solving state data, using an Object-Oriented static representation global “formal language” for 6. Use a communication with the other expert components The other expert componoats in general must : Be able to interpret the commands received by 1. the Process Expert and perform the appropriate tasks 2. Communicate the results using the “formal 1anguage” 3. Use Object-Oriented techniques for the static representation of data It is of great importance to utilise a uniform representation of the problem-solving state data, for the entire system, so that intercommunication between the various components could be based on a common perception about the state of the “environment” (forecasting process) and the way it is related to their internal structure and functioning. The basic concepts underlying the forecasting process include the data (time-series), their qualitative features (trend, seasonality, etc.), quantitative tools (forecasting methods), environmental factors (events affecting the underlying pattern) and evaluation of the results (error measures). Any forecasting methodology will always, more or less, deal with these concepts. Moreover, any course of reasoning applied on these concepts can be thought as a decision-making task, based on the contextual knowledge and involving judgement and expertise. As far as these are true, it seems reasonable and more convenient to utilise a data-based rather than an action-based structure for the development of a forecasting system.

Object-oriented

static representation

This general static representation scheme, aims at providing the guidelines upon which the structure of the whole system will be based. It consists of four major classes that may be slightly modified during the development of the system, but are most unlikely to dramatically change or cease to exist (Figure 2). The “Time-series” Class: The objects of this 1. class are the time series representing the historical data of the variable to be forecasted, or an independent (explanatory) variable. The properties of this class are some of the basic features of the series trend, seasonality, autocorrelation structure and stability of variance. Two subclasses are attached to the class of Time-series and are namely the Initial-time-series and the Modified-time series. Both of them inherit the properties of the parent class. The first one stands for the row data, while the second stores the new values of the properties after several modifications have been applied on the row data (differentiation, powertransformation, etc.)

M.

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Figure 2. The Object

oriented

representation


Proceedingsof the 28th Annual Hawaii International Conferenceon SystemSciences- 1995 2. The “Forecasting-methods” Class: This class includes four subclasses that correspond to the four most widely used families of forecasting models Regression, ARIMA and (Smoothing, Decomposition). We must notice that the external routines for the application of the above methods are stored in the model base to which the Model expert has direct access. Thus, the static representation includes only the basic elements of the various models, which are essential for reasoning while choosing and controlling while execnting the appropriate routines 3. The “EYQBIES”Class: The objects of this class are the environmental factors (events) affecting the time series underlying patterns. The properties of an event are: the type, the duration, the intensity and the effect it produced. Two subclasses are ittcorparated to the Events class. The first contains the single and the second the simultaneous events. Such a distinction is necessary for the storage of the modified values of the previously described properties, as the system explores the simultaneous effect of more than one eveuts. 4. Tbe “Results” Class: The properties of this class are: the time-series-name and the name of the method (method-name) which produced the results and they are inherited te the two subclasses, the one of Forecasts and the one of Error-measures. Objects belonging to the first subclass are the forecasts character&d by three additional properties: the horizon, the date to which they refer and their value. Finally, the subclass of Error-measures is divided into subclasses, the Within-sample aud two Out-of-sample, which include various error measures as objects (MSE, MAPE, etc.) The Formal

corresponding response types, with intercommunicating components. Communication

Process Expert

respect to the

between Process and Data Expert

Data Expert

laugmge

As we have already said, the Process expert is the component that maintains the problem-solving scheme and the only one that encapsulates an overall perception of the entire problem. Thus, the system represents a master-slave organisation wbere the Process expert (master) issues commands to the other components (slaves), triggering their reasoning mechanism to execute the received orders. In this sense a “formal language” for the intercommunication between the various components would consist of “commands” send from and “responses” directed to the Process expert. Their syntax consists of : i) a name describing the requested or executed action and ii) arguments providing the required specifications. The following table illustrates the basic concepts of the language and classifies the commands, along with the

Conclusions The paper has presented an architecture for an Intelligent Forecasting Information System (IFIS). The purpose of the architecture is to explore the benefits of integrating decision support and expert systems technologies in the area of business forecasting. This combination has been succesfully applied to corporate decision-making, through the use of Management Information System 0. We believe that forecasting process itself is a complicated decisionmaking task that involves expertise and requires judgement in order to select an appropriate forecasting model for extrapolation and interpret the results. Moreover, statistical forecasts should be judgmentally 175


Proceedingsof the 28th Annual Hawaii Intemadonal Conferenceon SystemSciences- 1995 adjusted in order to become reliable for critical corporate decisions to be based on them. The presented IFIS architecture is oriented to these goals as it provides the means for an effective management of quantitative information and the utilisation of qualitative information. The integration of the classical DSSs constituents in a forecasting system allows for the maintenance of more historical data and the incorporation of a greater variety of forecasting models. The handling of both models and data is enhanced by the Model and Data expert modules. Two learning mechanisms, provided by the Learning expert module, are ntilising historical information to assist the practitioner in method selection and judgmental adjustment phases, based on the feedback from previous results. Although these three modules provide some sort of expertise, their perceiving ability is confined within the limits of the specific tasks they are involved in. The Process expert module is the one that emulates the skills of an experienced forecaster and represents his/her reasoning througbout the forecasting process. The benefit from this approach is that the system is capable to adapt to any change in the course of reasoning, provided that the latter would be consistent with the forecasting process model presented in the paper.

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