An unsupervised neural network approach for imputation of missing values in univariate time series data

Handling missing values in time series data plays a key role in predicting and forecasting, as complete and clean historical data help to achieve higher accuracy. Numerous research works are present in multivariate time series imputation, but imputation in univariate time series data is least considered due to correlated variables unavailability. This article aims to propose an iterative imputation algorithm by clustering univariate time series data, considering the trend, seasonality, cyclical, and residue features of the data. The proposed method uses a similarity based nearest neighbor imputation approach on each clusters for filling missing values. The proposed method is evaluated on publicly available data set from the data market repository and UCI repository by randomly simulating missing patterns under low, moderate, and high missingness rates throughout the data series. The proposed method's outcome is evaluated with the imputeTestbench package with root mean squared error as an error metric and validated through prediction accuracy and concordance correlation coefficient statistical test. Experimental results show that the proposed imputation technique produces closer values to the original time series data set, resulting in low error rates compared with other existing imputation methods.