A proposed novel adaptive DC technique for non-stationary data removal
Bibliographic record
Abstract
The stationarity of a time series is an important assumption in the Box-Jenkins methodology. Removing the non-stationary feature from the time series can be done using a differencing technique or a logarithmic transformation approach, but it is not guaranteed from the first step. This paper proposes a new adaptive DC technique, a novel technique for removing a non-stationary time series from the first step. The technique involves transferring non-stationary data into another domain that deals with it as a stationary time series, as it is much easier to be forecasted in that domain. The adaptive DC technique has been applied to different time series, including gasoline and diesel fuel prices, temperature, demand side, inflation rate and number of internet users time series. The performance of the proposed technique is evaluated using different statistical tests, including Augmented Dickey-Fuller (ADF), Kwiatkowski-Phillips-Schmidt-Shin (KPSS), and Phillips Perron (PP). Additionally, the technique is validated by comparing it with a differencing technique, and the results show that the proposed technique slightly outperforms the differencing method. The importance of the proposed technique is its capability to get the stationarity data from the first step, whereas the differencing technique sometimes needs more than one step.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".