time series

Missing Data Imputation Using Generative Adversarial Nets

Missing data, especially missing data points in time series, are a pervasive issue for many applications relying on precise and complete data sets. For example in the financial sector, missing tick data can lead to deviating forecasts and thus to wrong decisions with high losses.

Interpretable Discrete Representation Learning on Time Series

Effective and efficient time series representation learning poses an important topic for a vast array of applications like, e.g. clustering. Many currently used approaches share the property of being difficult to interpret though. In many areas it is important that intermediate learned representations are easy to interpret for efficient downstream processing.