Machine Learning Based Approaches for Imputation in Time Series Data and their Impact on Forecasting
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 fitting any model for forecasting or prediction. Time series imputation remains a challenging task due to the existence of non-linear dependencies between current and past values. Conventional methods, such as deletion of rows containing missing values or filling them with the last observed value, add bias to the data and are therefore inefficient. There are situations where data is missing at consecutive points or random points in the dataset, and one particular method may not work well for all cases. In this paper, nine commonly used models in the field of imputation, based on tools of statistics, machine learning, and deep learning, are compared. Results show that Linear Memory Vector Gated Recurrent Unit (LIME-GRU) outperforms the other tested models by having the least Mean Square Error (MSE) and Root Mean Squared Error (RMSE). A predictive model to gauge the impact of imputation on prediction is also used to validate the findings. The results of the prediction model illustrate that with LIME-GRU, there was a 39% improvement in Average Aggregated Measure (AAGM) when compared with mode imputation on a particular test case.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".