Temperature Forecasts with Stable Accuracy in a Smart Home
Bibliographic record
Abstract
We forecast internal temperature in a home with sensors, modeled as a linear function of recent sensor values. When delivering forecasts as a service, two desirable properties are that forecasts have stable accuracy over a variety of forecast horizons – so service levels can be predicted – and that the forecasts rely on a modest amount of sensor history – so forecasting can be restarted soon after any data outage due to, for example, sensor failure. From a publicly available data set, we show that sensor values over the past one or two hours are sufficient to meet these demands. A standard machine learning method based on forward stepwise linear regression with cross validation gives forecasts whose out-of-sample errors increase slowly as the forecast horizon increases, and that are accurate to within one fifth of a degree C over three hours, and to within about one half degree C over six hours, based on one or two hours of history. Previous results from this data achieved errors within one degree C over three hours based on five days of history.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".