Versatile Deep Learning Based Application for Time Series Imputation
Bibliographic record
Abstract
It is common for a time series dataset to have missing values, and it is necessary to fill these missing elements before using the dataset for training forecasting models. Usually this problem is tackled using non-machine learning methods that introduce bias into the system which results in unreliable forecasting results. Moreover, most of the work found in the literature tackles imputation of missing values when they are randomly scattered in the dataset while very little work is found tackling the case of consecutive occurrence of missing data; i.e. missing data chunks in the dataset. Therefore, in this work, comprehensive imputation models are developed to impute both random as well as chunks of missing values. Alongside, a framework is found enabling the user to impute any time series data with the optimal models. In order to carry out the task, one non-deep machine learning model (Bidirectional Imputation model) and three deep learning (DL) imputation models (Ensemble model, Transfer Learning model and Hybrid model), are tested using complete time series. The results show that the hybrid model yields a maximum of 38% improvement in the Aggregate Error (AGE) when compared with other models.
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".