ForeCA implements Forecastable component analysis in R. For details on algorithm & methodology see Forecastable Component Analysis, JMLR, Goerg (2013).
In a nutshell: ForeCA finds linear combinations of multivariate time series that are most forecastable, where forecastability is measured by the spectral entropy of the resulting signal (linear combination of input).
You can install the stable version from CRAN:
install.packages('ForeCA')Alternatively, you can also install the latest version of ForeCA package directly from github as
library(devtools)
devtools::install_github("gmgeorg/ForeCA")
The workhorse function is ForeCA::foreca() which works just like the built-in
princomp function for PCA.
library(ForeCA)
citation("ForeCA")
For a tutorial on how to use foreca() and the entire ForeCA suite of
functions see the introductory
vignette
on CRAN.
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ForeCA references & applications in the literature (non-exhaustive; see here for full list of ForeCA citations)
- Very interesting application of ForeCA to historical time series data of temperature/climate to extract predictable climate signals. Fischer, Matt. (2016). Predictable components in global speleothem δ18O. Quaternary Science Reviews. 131. 380-392. 10.1016/j.quascirev.2015.03.024.
- ForeCA's forecastability measure, spectral entropy of a time series, can be useful as a feature to characterize/visualize/predict performance of different algorithms applied to a set of time series. Kang, Yanfei & Hyndman, Rob & Smith-Miles, Kate. (2017). Visualising forecasting algorithm performance using time series instance spaces. International Journal of Forecasting. 33. 345-358. 10.1016/j.ijforecast.2016.09.004.
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Cross-validated & SO posts (non-exhaustive)
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Blog posts (by others)
- Stock Forecasting with Machine Learning - Are Stock Prices Predictable? (2016/04/20)
- Are stocks predictable? (2014/02/20)