THETA MODEL FORECASTS REAL ESTATE VALUES
Bibliographic record
Abstract
The paper compares time series techniques that were used to forecast housing prices in UK, both at each region separately and as a whole. The tool used to provide forecasts is Theta Forecaster, a forecasting information system designed and developed at the Forecasting Systems Unit of National Technical University of Athens. This tool includes the Theta Model and several others, well-established forecasting methods. Theta Forecaster allows the combination of two or more techniques, which in many cases produces better forecasts than using a stand-alone method. The main feature of the Theta Model is that it applies different techniques to deal with sort-term and long-term forecasts and allows giving different weights in the sort and long-term components. The results show that Theta is always among the best forecasting methods and in many cases the most accurate one. The time series data used for forecasting is provided from the Halifax House Price Index, the UK's longest running monthly house price series covering the whole country from January 1983. The UK Index is derived from the mortgage data of the country's largest mortgage lender, which provides a robust and representative sample of the entire UK market. There are a number of national indices covering different categories of buyers (all, first-time buyers and home-movers) and houses, concerning the age (all, new and existing) and the type (all, detached, flats etc) of property. Regional indices for the 12 standard planning regions of the UK are available on a quarterly basis, while nationwide indices are produced monthly as well.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".