Meta-learning for vessel time series data imputation method recommendation

Abstract

A missing data problem is inevitable when collecting time series datasets from marine sensors. Due to this, sensor data is not reliable enough to assist decision-making. To impute missing values, a number of methods are available. Choosing the best imputation method, however, is not a trivial task, as it usually involves domain expertise and trial-and-error iterations. Additionally, if imputations are done carelessly, they produce a high error, resulting in incorrect assumptions by stakeholders. In this paper, a meta-learning approach is presented that can be used to extract characteristics of the underlying data, and based on that, a less error-prone imputation method is recommended. Ten commercial ocean-going vessel datasets are used to evaluate our proposed method. A total of 29,527 data samples were generated, comprising 22 inputs and 1 target. The proposed method achieves a weighted F1-Score of 87.5% when utilizing stratified 10-fold cross-validation. Our approach can improve the average imputation score up to 86%, with the worst-case improvement being 5%. This demonstrates that our proposed approach is efficient and effective in recommending the best imputation methods.