Fujitsu Limited and Inria, the French national research institute for digital science and technology, today announced the development of a new AI technology that can identify factors contributing to anomalies in time series data.
In recent years, various kinds of time-series data collected in fields including healthcare, social infrastructure, and manufacturing have been leveraged by AI to perform situational judgment and detect anomalies.
In the case of time-series data, however, there are a wide range of factors that can contribute to AI decision-making. This means that even experts find it difficult to notice what kind of changes in the data contributed to an anomaly detection making it difficult to take appropriate measures to prevent their occurrence.
Fujitsu and Inria, more specifically the Inria’s DATASHAPE Project Team led by Frédéric Chazal in France, have now successfully developed a new technology based on Topological Data Analysis (TDA) that can identify the factors contributing to anomaly detections by AI for time series data and visualize the differences in AI decisions during normal and anomalous circumstances.
Fujitsu and Inria anticipate that this will contribute to the analysis of the causes of anomalies in time series data for various phenomena, clarifying the mechanism surrounding the occurrence of anomalies, as well as the discovery of new solutions to these.
This technology will be presented as one of just 3% of total submitted papers as a “Long Talk” presentation at the Thirty-eighth International Conference on Machine Learning (ICML), the leading international conference in the field of machine learning, which opens virtually from July 18th, 2021.
