Abstract: Embodiments determine change points within received time series data exhibiting a natural trend. A first candidate change point comprising an earlier time and a first value, and a second candidate change point comprising a later time and a second value, are received with the time series data. A rule is executed upon the first candidate change point to calculate a first score, and executed upon the second candidate change point to calculate a second score. The rule comprises a primary criterion for a change direction relative to the natural trend, a secondary criterion for a change position within the time series data, and a tertiary criterion for a change magnitude. The first score is compared to the second score to select the first candidate change point or the second candidate change point as a determined change point. The determined change point is stored for use in subsequent data analysis.

Abstract: In an example embodiment, an additional classifier is introduced to an autoencoder neural network. The additional classifier performs an additional classification task during the training and testing phases of the autoencoder neural network. More precisely, the autoencoder neural network learns to classify the domain (or origin) of each specific input sample. This leads to additional contextual awareness in the autoencoder neural network, which improves the reconstruction quality during both the training and testing phases. Thus, the technical problem of decreased autoencoder neural network reconstruction quality caused by high data variance is addressed.