https://github.com/gagniuc/spectral-forecast-model-for-signals
Spectral forecast is a novel general-purpose prediction model recently published in the journal CHAOS. 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.
https://github.com/gagniuc/spectral-forecast-model-for-signals
alternative chaos equation html5 information javascript js math matrices measure model outcomes prediction signal signals space spectral-forecast states
Last synced: 7 months ago
JSON representation
Spectral forecast is a novel general-purpose prediction model recently published in the journal CHAOS. 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.
- Host: GitHub
- URL: https://github.com/gagniuc/spectral-forecast-model-for-signals
- Owner: Gagniuc
- License: mit
- Created: 2021-10-28T21:24:29.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-03-21T10:54:19.000Z (over 3 years ago)
- Last Synced: 2025-01-15T07:31:49.683Z (9 months ago)
- Topics: alternative, chaos, equation, html5, information, javascript, js, math, matrices, measure, model, outcomes, prediction, signal, signals, space, spectral-forecast, states
- Language: HTML
- Homepage:
- Size: 167 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE.md
Awesome Lists containing this project
README
# Spectral Forecast model for signals
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 (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 (A and B). 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 (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). 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:
- [Spectral forecast: A general purpose prediction model as an alternative to classical neural networks](https://aip.scitation.org/doi/10.1063/1.5120818)
- [Algorithms in Bioinformatics: Theory and Implementation](https://www.wiley.com/en-ag/Algorithms+in+Bioinformatics%3A+Theory+and+Implementation-p-9781119697961)
# Live demo
**Live demo**: https://gagniuc.github.io/Spectral-Forecast-model-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.