**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).

**UPDATE**: As of 2020-06-09 **ForeCA** has
been removed from CRAN, because the **ifultools** /
**sapa** dependecies are no longer maintained. I am working
on an update to **ForeCA** to not rely on these packages,
but only rely on **astsa** for multivariate specturm
estimation. See `NEWS.md`

for details.

In the meantime you can install the ForeCA package directly from github as

```
library(devtools)
devtools::install_github("gmgeorg/ForeCA")
```

**Temporarily not working**

You can install the stable version on CRAN:

`install.packages('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.

**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.

- Very interesting application of ForeCA to historical time series
data of temperature/climate to extract predictable climate signals. Fischer,
Matt. (2016).
**Cross-validated & SO posts**(non-exhaustive)**Blog posts**(by others)- Stock Forecasting with Machine Learning - Are Stock Prices Predictable? (2016/04/20)
- Are stocks predictable? (2014/02/20)