Time series data are observations collected over time intervals. Successful analysis of time series data captures patterns such as trends, cyclicity and irregularity, which are crucial for decision making in research, business, and governance. However, missing values in time series data occur often and present obstacles to successful analysis, thus they need to be filled with alternative values, a process called imputation. Although various approaches have been attempted for robust imputation of time series data, even the most advanced methods still face challenges including limited scalability, poor capacity to handle heterogeneous data types and inflexibility due to requiring strong assumptions of data missing mechanisms. Moreover, the imputation accuracy of these methods still has room for improvement. In this study, I developed tsDataWig (time-series DataWig) by modifying DataWig, a neural network-based method that possesses the capacity to process large datasets and heterogeneous data types but was designed for non-time series data imputation. Unlike the original DataWig, tsDataWig can directly handle values of time variables and impute missing values in complex time series datasets. Using one simulated and three different complex real-world time series datasets, I demonstrated that tsDataWig outperforms the original DataWig and the current state-of-the-art methods for time series data imputation and potentially has broad application due to not requiring strong assumptions of data missing mechanisms. This study provides a valuable solution for robustly imputing missing values in challenging time series datasets, which often contain millions of samples, high dimensional variables, and heterogeneous data types.

Most datasets suffer from partial or complete missing values, which has downstream limitations on the available models on which to test the data and on any statistical inferences that can be made from the data. Several imputation techniques have been designed to replace missing data with stand in values. The various approaches have implications for calculating clinical scores, model building and model testing. The work showcased here offers a novel means for categorical imputation based on item response theory (IRT) and compares it against several methodologies currently used in the machine learning field including k-nearest neighbors (kNN), multiple imputed chained equations (MICE) and Amazon Web Services (AWS) deep learning method, Datawig. Analyses comparing these techniques were performed on three different datasets that represented ordinal, nominal and binary categories. The data were modified so that they also varied on both the proportion of data missing and the systematization of the missing data. Two different assessments of performance were conducted: accuracy in reproducing the missing values, and predictive performance using the imputed data. Results demonstrated that the new method, Item Response Theory for Categorical Imputation (IRTCI), fared quite well compared to currently used methods, outperforming several of them in many conditions. Given the theoretical basis for the new approach, and the unique generation of probabilistic terms for determining category belonging for missing cells, IRTCI offers a viable alternative to current approaches.

While training a Machine Learning model the quality of the model is directly proportional to the quality of data. However, in some cases, there are a lot of missing values in the dataset affecting the quality of prediction in the long run. Several methods can be used to fill the missing values and Datawig is one of the most efficient ones. Datawig is a Deep Learning library developed by AWS Labs and is primarily used for "Missing Value Imputation". The library uses "mxnet" as a backend to train the model and generate the predictions.