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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.<\/jats:p>","DOI":"10.1108\/02635570310506133","type":"journal-article","created":{"date-parts":[[2003,12,7]],"date-time":"2003-12-07T03:35:18Z","timestamp":1070768118000},"page":"711-726","source":"Crossref","is-referenced-by-count":26,"title":["Theta intelligent forecasting information system"],"prefix":"10.1108","volume":"103","author":[{"given":"K.","family":"Nikolopoulos","sequence":"first","affiliation":[{"name":"Forecasting Systems Unit, School of Electrical & Computer Engineering, National Technical University of Athens, Athens, Greece"}]},{"given":"V.","family":"Assimakopoulos","sequence":"additional","affiliation":[{"name":"Forecasting Systems Unit, School of Electrical & Computer Engineering, National Technical University of Athens, Athens, Greece"}]}],"member":"140","reference":[{"issue":"1","key":"2025072819502089500_B1","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/S0169-2070(99)00034-5","article-title":"Corrections to rule-based forecasting: findings from a replication","volume":"16","author":"Adya","year":"2000","journal-title":"International Journal of Forecasting"},{"issue":"4","key":"2025072819502089500_B2","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1016\/S0169-2070(00)00074-1","article-title":"An application of rule-based forecasting to a situation lacking domain knowledge","volume":"16","author":"Adya","year":"2000","journal-title":"International Journal of Forecasting"},{"issue":"2","key":"2025072819502089500_B3","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S0169-2070(01)00079-6","article-title":"Automatic identification of time series features for rule-based forecasting","volume":"17","author":"Adya","year":"2001","journal-title":"International Journal of Forecasting"},{"issue":"7","key":"2025072819502089500_B4","doi-asserted-by":"crossref","DOI":"10.1108\/02635579910291984","article-title":"Using a neural network to forecast inflation","volume":"99","author":"Aiken","year":"1999","journal-title":"Industrial Management & Data Systems"},{"key":"2025072819502089500_B5","doi-asserted-by":"crossref","unstructured":"Armstrong, J.S.\n           (2001), Principles of Forecasting: A Handbook for Researchers and Practitioners, Kluwer Academic Publishers, Dordrecht.","DOI":"10.1007\/978-0-306-47630-3"},{"key":"2025072819502089500_B6","doi-asserted-by":"crossref","unstructured":"Armstrong, J.S.\n           and Collopy, F. 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