Imputation of Missing Values in Daily Wind Speed Data Using Hybrid AR-ANN Method
Bibliographic record
Abstract
Wind speed data collection process faces several problems as failure of data observing devices. Therefore, windspeed data naturally contains missing values. Imputing these missing values using an effective method isimportant before performing time series analysis. The classical methods as linear, nearest neighbor, and statespace may not provide accurate imputations when the wind speed contains nonlinearity. In this study, the hybridartificial neural network (ANN) and autoregressive (AR) method is proposed for imputing the missing values.ANN is a nonlinear method that is capable of imputing the missing values in wind speed data with nonlinearcharacteristic. AR model is used for determining the structure of the input layer for the ANN. Listwise deletion isused before AR modeling to handle the missing values. A case study is carried out using daily Iraqi andMalaysian wind speed data. The proposed imputation method is compared with linear, nearest neighbor, andstate space methods. The comparison has shown that AR-ANN outperformed the classical methods. Inconclusion, the missing values in wind speed data with nonlinear characteristic can be imputed more accuratelyusing AR-ANN. Therefore, imputing the missing values using AR-ANN leads to more accurate performance oftime series modeling and analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".