Spectral Forecast equation for signals

One of the easiest applications to understand the spectral forecast equation is related to signals. A question that can be asked would be: given two signals A and B, what would a third signal M look like if it must resemble both signals in a certain proportion? For instance, a signal M may be required to resemble signal A in a proportion of 23% and signal B in a remaining proportion of 77%. This is exactly what the spectral equation provides, namely a direct way of obtaining such an intermediary signal. Moreover, the Spectral Forecast equation can also be applied to matrices (two dimensions), but also to mathematical objects with more than two or three dimensions.

Info on Spectral Forecast

Please read more about Spectral Forecast here:

• Spectral forecast: A general purpose prediction model as an alternative to classical neural networks

• Algorithms in Bioinformatics: Theory and Implementation

Live demo

Live demo: https://gagniuc.github.io/Spectral-Forecast-equation-for-signals/

References

• Paul A. Gagniuc et al. Spectral forecast: A general purpose prediction model as an alternative to classical neural networks. Chaos 30, 033119 (2020).

• Paul A. Gagniuc. Algorithms in Bioinformatics: Theory and Implementation. John Wiley & Sons, Hoboken, NJ, USA, 2021, ISBN: 9781119697961.