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 # Spectral Forecast model for signals
 
-This Javascript project uses my own mathematical model published in the journal [Chaos](https://aip.scitation.org/doi/10.1063/1.5120818). The model is called Spectral Forecast. The Spectral Forecast equation is a part of the Spectral Forecast model and it was initially used on matrices. It can also be used on other multidimensional mathematical objects. Here, a novel utility is demonstrated for signals by using the equation on vectors. This new use on 1-dimensional objects was published [here](https://www.wiley.com/en-ie/Algorithms+in+Bioinformatics:+Theory+and+Implementation-p-9781119697992). <b>Signal processing with Spectral Forecast</b> - is a demo application designed in Javascript, that is able to mix two signals (<i>A</i> and <i>B</i>) in arbitrary proportions. Different cases can be seen, with two different waveform signals that are combined depending on a value <i>d</i>, called a distance. This distance <i>d</i> can be arbitrary chosen between zero and a value Max(d), which is defined as the maximum value found above the two vectors that represent these signals. Note that the construction and theory behind the chart of this application can be found [here](https://github.com/Gagniuc/World-smallest-js-chart-v1.0).
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-Spectral forecast is a novel general-purpose prediction model recently published in the journal CHAOS. This chapter explains how spectral forecast works
-in detail and suggests some interesting possible applications of the method in bioinformatics. Here, this novel prediction method is described, implemented, and tested. The model revolves around three known states: two extreme outcomes (A and B) and one measurement (P). These states are represented by either vectors or matrices that include sets of homologous parameters. An information spectrum is described as a series of predicted states (M1, M2, M3, …, Md) generated between the two extreme outcomes (Aand B). These states are successively calculated using the spectral forecast equation. The predicted states are compared with a known state (P) from the measurements to generate a series of similarity index values. The trend generated by the values of the similarity index shows how a system may behave against these two extreme outcomes (A and B)
+Spectral forecast is a novel general-purpose prediction model recently published in the journal [CHAOS](https://doi.org/10.1063/1.5120818). The model revolves around three known states: two extreme outcomes (<i>A</i> and <i>B</i>) and one measurement (<i>P</i>). These states are represented by either vectors or matrices that include sets of homologous parameters. An information spectrum is described as a series of predicted states (M1, M2, M3, …, Md) generated between the two extreme outcomes (<i>A</i> and <i>B</i>). These states are successively calculated using the [spectral forecast equation](https://github.com/Gagniuc?tab=repositories&q=spectral+forecast&type=&language=&sort=). The predicted states are compared with a known state (<i>P</i>) from the measurements to generate a series of similarity index values. The trend generated by the values of the similarity index shows how a system may behave against these two extreme outcomes (<i>A</i> and <i>B</i>). Note that the construction and theory behind the chart of this application can be found [here](https://github.com/Gagniuc/World-smallest-js-chart-v1.0).
 
 # Info on Spectral Forecast
  Please read more about Spectral Forecast here: