Solve time series problems more accurately and efficiently with LMUs
LMUs have been proven to generate optimal, compressed representations of temporal information. This allows their efficient and scale-invariant representations to be used to solve real world problems better than alternative approaches, including LSTMs, GRUs, and Transformers. LMUs can be trained and deployed with traditional deep learning techniques on hardware you already have, or can be deployed on neuromorphic hardware or neural accelerators for massive power savings.
We have worked with many clients to develop LMU-based AI systems to solve their time series problems more accurately and efficiently than any other solution on the market today. To ensure customers get the best results, we often start by implementing standard state-of-the-art techniques first, as a baseline to compare to. We have yet to find a time series application in which an LMU-based architecture is not the best option. This is true whether the client is focussed on problems in health care, manufacturing, cyber security, robotics, predictive maintenance, financial prediction, sensor data processing, or speech recognition.
If you have a time series problem that you’re struggling with, let us show you the power of LMUs firsthand.