We were tired of the lack of transparency and robustness of AI in financial time series.
So we created FMI
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Unlike standard AI algorithms, FMI contains only the degrees of freedom relevant for a given task. This makes our algorithms exponentially faster and also more accurate (in fact saturates the accuracy bound for a given data set and computational power). We can deal with any degree of non-linearity and non-stationarity.
Our algorithms are not black boxes. You have full analytical control.
Our patented technology ensures that the algorithm does not overfit or underfit to the data. Forget about curve fitting in a slow and tedious fashion with neural networks. Our algorithms are fast and capture exactly the amount of signal in the data-no more and no less.
Born in the Mathematics Department of Princeton, FMI Technologies began as an attempt to provide a much more robust and transparent alternative to Artificial Intelligence, Field Machine Intelligence (FMI), for time series forecasting and classification, anomaly detection, and correlation analysis. Today we aid hedge funds, demand forecasting and supply chain firms, as well as any one interested in time series and other structured datasets. Having partnered with Princeton, IAS and UC Berkeley, we have used the sharpest minds at our disposal to combine tools from chaos theory, AI, field theory and control theory to create what is now known as Field Machine Intelligence (FMI). Our models train quickly and satisfy the accuracy bound. They are efficient at arbitrarily nonlinear, non-stationary and non-IID datasets and are built to account for sudden regime shifts.
Effective field theory, quantum theory, renormalisation group artificial intelligence machine learning big data, time series forecasting, anomaly detection, hedge fund, algorithmic trading