https://github.com/gmgeorg/foreca
ForeCA: Forecastable Component Analysis in R
https://github.com/gmgeorg/foreca
blind-source-separation dimensionality-reduction forecasting multivariate-timeseries signal-processing spectrum time-series time-series-analysis
Last synced: 4 months ago
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ForeCA: Forecastable Component Analysis in R
- Host: GitHub
- URL: https://github.com/gmgeorg/foreca
- Owner: gmgeorg
- Created: 2020-06-07T20:11:47.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-07-03T02:41:35.000Z (almost 6 years ago)
- Last Synced: 2025-12-09T20:37:28.510Z (6 months ago)
- Topics: blind-source-separation, dimensionality-reduction, forecasting, multivariate-timeseries, signal-processing, spectrum, time-series, time-series-analysis
- Language: R
- Homepage:
- Size: 2.04 MB
- Stars: 16
- Watchers: 1
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ForeCA R package
[](https://cran.r-project.org/package=ForeCA)
**ForeCA** implements *Forecastable component analysis* in R. For details on
algorithm & methodology see [*Forecastable Component Analysis*, JMLR, Goerg
(2013)](http://proceedings.mlr.press/v28/goerg13.pdf).
**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).
## Installation
You can install the stable version from
[CRAN](https://cran.r-project.org/package=ForeCA):
```r
install.packages('ForeCA')
```
Alternatively, you can also install the latest version of **ForeCA** package
directly from github as
```{r}
library(devtools)
devtools::install_github("gmgeorg/ForeCA")
```
## Usage
The workhorse function is `ForeCA::foreca()` which works just like the built-in
`princomp` function for PCA.
```{r}
library(ForeCA)
citation("ForeCA")
```
For a tutorial on how to use `foreca()` and the entire **ForeCA** suite of
functions see the [introductory
vignette](https://CRAN.R-project.org/package=ForeCA/vignettes/Introduction.html)
on CRAN.
## References
* **ForeCA references & applications in the literature** (non-exhaustive; see here for [full list of ForeCA citations](https://scholar.google.com/scholar?client=ubuntu&channel=fs&oe=utf-8&um=1&ie=UTF-8&lr&cites=5674198772479433271))
* 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.](https://www.researchgate.net/publication/275953571_Predictable_components_in_global_speleothem_d18O)
* 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.](https://isidl.com/wp-content/uploads/2017/06/E3999-ISIDL.pdf)
* **Cross-validated & SO posts** (non-exhaustive)
* [How to determine forecastability of time
series](https://stats.stackexchange.com/questions/126829/how-to-determine-forecastability-of-time-series)
* **Blog posts** (by others)
* [Stock Forecasting with Machine Learning - Are Stock Prices
Predictable?](http://www.anlytcs.com/2016/04/stock-forecasting-with-machine-learning.html)
(2016/04/20)
* [Are stocks predictable?](http://fastml.com/are-stocks-predictable/)
(2014/02/20)