Fujitsu Deep Learning Technology Analyzes Time-series Data with High Precision

Kawasaki, Japan — Fujitsu Laboratories has developed an approach to deep learning that uses advanced chaos theory and topology to automatically and accurately classify volatile time-series data. Demonstrating promise for Internet-of-Things applications, time-series data can also be subject to severe volatility, making it difficult for people to discern patterns in the data. Deep learning technology, which is attracting attention as a breakthrough in the advance of artificial intelligence, has achieved extremely high recognition accuracy with images and speech, but the types of data to which it can be applied is still limited. In particular, it has been difficult to accurately and automatically classify volatile time-series data — such as that taken from IoT devices — of which people have difficulty discerning patterns. In benchmark tests, held at UC Irvine Machine…


Link to Full Article: Fujitsu Deep Learning Technology Analyzes Time-series Data with High Precision