Theta intelligent forecasting information system

The need effectively to 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. Explores the potential benefits of such integration in the area of business forecasting. Describes the forecasting process and identifies its main functional elements. Some of these elements provide the requirements for an intelligent forecasting support system. Describes the architecture and the implementation of such a system, the theta intelligent forecasting information system (TIFIS) that that first‐named author had developed during his dissertation. In TIFIS, besides the traditional components of a decision‐support onformation system, four constituents are included that try to model the expertise required. The information system adopts an object‐oriented approach to forecasting and exploits the forecasting engine of the theta model integrated with automated rule based adjustments and judgmental adjustments. Tests the forecasting accuracy of the information system on the M3‐competition monthly data.

[1]  J. Ord,et al.  Principles of forecasting: A handbook for researchers and practitioners , 2002 .

[2]  Monica Adya,et al.  Corrections to rule-based forecasting: findings from a replication , 2000 .

[3]  Roger S. Pressman,et al.  Software Engineering: A Practitioner's Approach , 1982 .

[4]  Kostas S. Metaxiotis,et al.  Etifis: An Innovative e-Forecasting Web Application , 2003, Int. J. Softw. Eng. Knowl. Eng..

[5]  A. H. Lines Forecasting ‐ key to good service at low cost , 1996 .

[6]  Binshan Lin,et al.  Building Intelligent Forecasting Systems in Manufacturing , 1989 .

[7]  T. A. Spedding,et al.  Forecasting demand and inventory management using Bayesian time series , 2000 .

[8]  Benito E. Flores,et al.  The use of an expert system in the M3 competition , 2000 .

[9]  P. Goodwin,et al.  On the asymmetry of the symmetric MAPE , 1999 .

[10]  Efraim Turban,et al.  Integrating Expert Systems and Decision Support Systems , 1986, MIS Q..

[11]  Stephen A. DeLurgio,et al.  Forecasting systems for operations management , 1991 .

[12]  J. Keith Ord,et al.  Automatic neural network modeling for univariate time series , 2000 .

[13]  Amrik S. Sohal,et al.  Forecasting: The Key to Managerial Decision Making , 1994 .

[14]  Robert D. Klassen,et al.  Forecasting practices of Canadian firms: Survey results and comparisons , 2001 .

[15]  Ângela Denise Lemos,et al.  Technological forecasting techniques and competitive intelligence: tools for improving the innovation process , 1998 .

[16]  John F. Rockart,et al.  Executive Support Systems: The Emergence of Top Management Computer Use , 1988 .

[17]  Gregoris Mentzas,et al.  An architechture for intelligent assistance in the forecasting process , 1995, Proceedings of the Twenty-Eighth Annual Hawaii International Conference on System Sciences.

[18]  Paul J. Fields Forecasting systems for operations management: Stephen A. Delurgio and Carl D. Bhame, 1991, (Business One Irwin, Homewood, IL), pp. 648, hardback, US$49.95 , 1992 .

[19]  Fred Collopy,et al.  Causal Forces: Structuring Knowledge for Time-Series Extrapolation , 1993 .

[20]  Anthony C. Stylianou,et al.  Expert support systems: integrating AI technologies , 1993, CACM.

[21]  John G. Wacker,et al.  Sales forecasting for strategic resource planning , 2002 .

[22]  Hans W. Gottinger,et al.  Intelligent decision support systems , 1992, Decis. Support Syst..

[23]  Gerhard Steinke,et al.  Business rules as the basis of an organization's information systems , 2003, Ind. Manag. Data Syst..

[24]  Alavi Ackoff,et al.  Decision support systems research : current state and trends , 1999 .

[25]  Konstantinos Nikolopoulos,et al.  The theta model , 1999 .

[26]  John G. Wacker,et al.  Forecasting accuracy: comparing the relative effectiveness of practices between seven developed countries , 1998 .

[27]  Efraim Turban,et al.  Decision Support and Expert Systems: Management Support Systems , 1990 .

[28]  Keith Ridgway,et al.  A computer‐aided inventory management system – part 1: forecasting , 1995 .

[29]  Jae Kyu Lee,et al.  Judgmental adjustment in time series forecasting using neural networks , 1998, Decis. Support Syst..

[30]  Alan J. Beckett,et al.  Implementing an industrial continuous improvement system: a knowledge management case study , 2000 .

[31]  Leonard J. Tashman,et al.  Automatic forecasting software: A survey and evaluation☆ , 1991 .

[32]  Nada R. Sanders,et al.  Managing the forecasting function , 1995 .

[33]  Robert Fildes,et al.  Forecasting Systems for Production and Inventory Control , 1992 .

[34]  Jinxing Xie,et al.  Production, Manufacturing and Logistics The impact of forecasting model selection on the value of information sharing in a supply chain , 2002 .

[35]  D. King Intelligent decision support: strategies for integrating decision support, database mangement and expert system , 1990 .

[36]  Shouhong Wang,et al.  Designing information systems for electronic commerce , 2001, Ind. Manag. Data Syst..

[37]  Biren Prasad,et al.  Converting computer-integrated manufacturing into an intelligent information system by combining CIM with concurrent engineering and knowledge management , 2000, Ind. Manag. Data Syst..

[38]  John S. Edwards,et al.  Expert systems in management and administration - are they really different from Decision Support Systems? , 1992 .

[39]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[40]  K. Nikolopoulos,et al.  The theta model: a decomposition approach to forecasting , 2000 .

[41]  Fred Collopy,et al.  Rule-Based Forecasting: Development and Validation of an Expert Systems Approach to Combining Time Series Extrapolations , 1992 .

[42]  Jeretta Horn Nord,et al.  Information systems project development: knowledge and domain requirements for the systems analyst , 1997 .

[43]  Fred Collopy,et al.  Automatic Identification of Time Series Features for Rule-Based Forecasting , 2001 .

[44]  D. T. Brownlie,et al.  The Role of Technology Forecasting and Planning: Formulating Business Strategy , 1992 .

[45]  Guy Melard,et al.  Automatic ARIMA modeling including interventions, using time series expert software , 2000 .

[46]  Nigel Meade,et al.  A note on the Robust Trend and ARARMA methodologies used in the M3 Competition , 2000 .

[47]  Konstantinos Nikolopoulos,et al.  Forecasting international tourist flows into Hungary , 2001 .

[48]  Gene Fliedner,et al.  Hierarchical forecasting: issues and use guidelines , 2001, Ind. Manag. Data Syst..

[49]  Gregoris Mentzas,et al.  Object-based intelligence in office and production processes: a view on integration , 1994 .

[50]  Kostas S. Metaxiotis,et al.  GENESYS: an expert system for production scheduling , 2002, Ind. Manag. Data Syst..

[51]  Gregory Mentzas,et al.  Towards intelligent organisational information systems , 1994 .

[52]  Fred Collopy,et al.  An Application of Rule-Based Forecasting to a Situation Lacking Domain Knowledge , 2000 .

[53]  Klaus Schrape,et al.  The business of forecasting , 2001 .

[54]  Vassilis Assimakopoulos,et al.  An object oriented approach to forecasting , 1992 .

[55]  R. Balachandra,et al.  An expert system for new product development projects , 2000 .

[56]  Robert J. Vokurka,et al.  Automatic feature identification and graphical support in rule-based forecasting: a comparison , 1996 .

[57]  John Walker,et al.  Decision support for the single-period inventory problem , 2000, Ind. Manag. Data Syst..

[58]  Lech J. Janczewski,et al.  An information system for operations management: co‐ordination and integration , 1998 .

[59]  M. Aiken Using a neural network to forecast inflation , 1999 .