SANNI: Online Imputation of Missing Values in Multivariate Time Series Based on Deep Learning and Behavioral Patterns

Currently, in a wide spectrum of applications, to avoid the processing and analysis of incomplete time series, end-users need efficient and accurate approaches to online imputation of missing values. In the article, we introduce a novel method called SANNI (snippet and artificial neural network-based imputation) for the recovery of missing values in multivariate time series coming online. SANNI leverages behavioral patterns (called snippets) that are subsequences representing an actor’s typical activities, which are reflected by the time series. Preprocessing is performed for each series of a representative fragment of the input data, where we normalize all the subsequences with non-NaN values and discover snippets. To impute, our method employs two deep learning models: Recognizer and Reconstructor. Given a multivariate subsequence ended by a missing value, Recognizer outputs a snippet to which the subsequence of the series is the most similar. Reconstructor, for each series, imputes missing values using the snippet discovered previously and results taken from Recognizer. In the extensive experiments, SANNI on average outperforms state-of-the-art competitors over time series from diverse subject domains related to an actor with predefined activities as well as under the blackout scenario.