We present DITAN, a novel unsupervised domain-agnostic framework for detecting and interpreting temporal-based anomalies. It is based on an encoder-decoder architecture with both implicit/explicit attention and adjustable layers/units for predicting normality as regular patterns in sequential data. A two-stage thresholding methodology with built-in pruning is used to detect anomalies, while root cause and similarities are interpreted in data and units space. Our approach is designed to intersect the 9 fundamental characteristics extracted from the union of related works. We demonstrate the DITAN modules on real-world datasets of 6 multivariate time series contaminated by point and contextual temporal-based anomalies at a varying duration. Experiments show a dominant predictability power of DITAN against the originally proposed models. DITAN is able to determine critical regions and thus identify anomalous events similarly well. Informative similarities between anomalous records are interpreted, since almost all similarities in units space are also verified in data space.