A Method for Improving Imputation and Prediction Accuracy of Highly Seasonal Univariate Data with Large Periods of Missingness
Bibliographic record
Abstract
Imputation of missing data in datasets with high seasonality plays an important role in data analysis and prediction. Failure to appropriately account for missing data may lead to erroneous findings, false conclusions, and inaccurate predictions. The essence of a good imputation method is its missingness-recovery-ability, i.e., the ability to deal with large periods of missing data in the dataset and the ability to extract the right characteristics (e.g., seasonality pattern) buried under the dataset to be analyzed. Univariate imputation is usually incapable of providing a reasonable imputation for a variable when periods of missing values are large. On the other hand, the default multivariate imputation approach cannot provide an accurate imputation for a variable when missing values of other correlated variables used for imputation occur at exactly the same time intervals. To deal with these drawbacks and to provide feasible imputations in such scenarios, we propose a novel method that converts a single variable into a multivariate form by exploiting the high seasonality and random missingness of this variable. After this conversion, multivariate imputation can then be applied. We then test the proposed method on an LTE spectrum dataset for imputing a single variable, such as the average cell throughput. We compare the performance of our proposed method with Kalman filtering and default method for multivariate imputation. The performance evaluation results clearly show that the proposed method significantly outperforms Kalman filtering and default method in terms of imputation and prediction accuracy.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 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".