AFMF: Time series anomaly detection framework with modified forecasting

Abstract

Forecasting-based method is one of prevalent unsupervised time series anomaly detection approaches. Currently, large portions of existing forecasting-based methods are devoted to discussing the feature extraction of input sequences and targeting at accurate predictions. In essence, their frameworks and core ideas are identical to the pure forecasting models. However, the distinctiveness of anomalies is affected by not only forecasting accuracy, but also many other factors. This paper summarizes three other dominant factors: (1) Scale disparity; (2) Discrete variate; (3) Input anomaly. They are common and non-negligible in real-world anomaly detection. Moreover, we propose AFMF: a time series Anomaly detection Framework with Modified Forecasting to solve them respectively by its three key components, i.e., Local Instance Normalization, Lopsided Forecasting and Progressive Adjacent Masking. The first two are refined descendants of existing mechanisms while the third component is completely novel. Extensive experiments on ten benchmarks verify that AFMF can be combined with any forecasting or forecasting-based anomaly detection method to achieve SOTA anomaly detection performances. The source code is available at https://github.com/OrigamiSL/AFMF.