Tackling Imputation Across Time Series Models Using Deep Learning and Ensemble Learning

Missing data are commonly found in time series datasets. These missing elements are usually a hurdle in utilizing the datasets in prediction or forecasting, making imputation of those missing values imperative. Due to the non-linear dependencies between the current and previous values, imputation remains a challenging task. Conventional methods such as averaging, deletion or filling with the last observed value add bias to the data and are therefore inefficient. Since different time series showcase varying characteristics, figuring out which imputation method works best for the respective time series is essential. In this work, seven different deep learning (DL) imputation methods are examined along with three machine learning (ML) ensembles. To enable a recommendation of the best imputation method for each time series type, the imputation models are tested using the four main types of time series: trend (T), seasonal (S), combined trend and seasonal time series (T&S) and random (R) time series. Results indicate that the Gated Recurrent Unit (GRU) neural networks are, in general, the best for missing values imputation with varying complexity based on the time series type. For example, it is found that the residual GRU is recommended for the trend and seasonal time series while the GRU is recommended for the combined type. Conversely, all tested DL imputation models can be used with the random time series type. In addition, the considered ML ensembles do not perform as high as the DL models with all tested types of times series.