A more complex example that uses the spectral forecast equation is related to matrices. A question that can be asked this time would be: given two matrices A and B, what would a third matrix (M) look like if it must resemble both A and B in a certain proportion? For instance, a matrix M may be required to resemble matrix A in a proportion of 60% and matrix B for the remaining proportion of 40%. This is exactly what the spectral equation provides, namely a direct way of obtaining such an intermediary matrix. Moreover, the Spectral Forecast equation can also be applied to signals (one dimension), but also to mathematical objects with more than two dimensions. More projects involving the Spectral Forecast equation can be found in multiple languages here.
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
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Paul A. Gagniuc et al. Spectral forecast: A general purpose prediction model as an alternative to classical neural networks. Chaos 30, 033119 (2020).
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Paul A. Gagniuc. Algorithms in Bioinformatics: Theory and Implementation. John Wiley & Sons, Hoboken, NJ, USA, 2021, ISBN: 9781119697961.