Time series prediction is important in various fields- weather prediction, traffic control, financial prediction, as well as innumerable areas of physics. Yet it has been observed that the various avatars of AI have mostly underperformed at time series prediction (see de Prado, 2017). In the paper, he explains various roadblocks faced while implemented ML algorithms for time series analysis, especially for financial time series. He proposes many partial solutions, but most of the problems are inherent to ML and difficult to combat. Enter FMI- a technique combining elements of chaos theory and stochastic methods in quantum field theory to be inherently free of these problems. The development of field theory closely mirrored that of modern quantum and statistical field theory. To say that it is one of the most powerful tools to model natural phenomena that are mostly noisy, nonlinear and chaotic, is an understatement. We expound their power vis a vis ML methods that have been shown to be ineffectual in de Prado’s paper. Quoting de Prado, “When misused, ML algorithms will confuse statistical flukes with patterns. This fact, combined with the low signal-to-noise ratio that characterizes finance, all but ensures careless users will produce false discoveries at an ever-greater speed.”
On the other hand, FMI never underfits not overfits to a given dataset, extracting only as much statistical signal as is present. Consequently, it is easily shown that it satisfies the accuracy bound defining the limit to which time series can be predicted. The following presents the ten pitfalls encountered in the application of ML based models to time series (mirroring de Prado), along with discussions on how FMI deals with them.
Other unique advantages of FMI include:
Files coming soon.