Advanced Forecasting Information System - PYTHIA: Application in Real Estate
Bibliographic record
Abstract
This paper presents an Advanced Forecasting Information System - PYTHIA - estimating real estate values. PYTHIA was developed with MS Visual Basic.NET and Dundas Chart for .NET. Database was developed with MS SQL Server 2000. System implementation includes the processes of sub-system development according their architecture. Moreover, the real interconnection of these sub-systems and their integration in a general complete and functional system is realised. Sub-systems implementation is specific for each module and their particular functions. The sub-systems are developed independent, according to specific requirements so that their interconnection will become feasible. System innovation focuses on sub-systems implementation and methodology. Sub-systemís that integrated in Pythia are: Data Adjustments, Data analysis, Special Events / Actions (SEA), Forecasting methods, Forecasts, Monitoring and Reporting sub-system. The applicability of the system was tested with real database real estate values. It was used in order to test and evaluate the IT system. Data is used here, which represents the total average dwelling prices of U.K. and is organized in months, from January 1983 up to September 2006. This paper will examine the present state in UK house market and will test the estimation methods on the monthly data. The time series data used for forecasting is again provided from the Halifax House Price Index and covers different categories of buyers (all, first-time buyers and home-movers) and houses (all, new and existing).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".