*rego* is a machine learning algorithm for predicting and imputing time series. It can automatically set all the parameters needed, thus in the minimal configuration it only requires the target variable and the dependent variables if present. It can address large problems with hundreds or thousands of dependent variables and problems in which the number of dependent variables is greater than the number of observations. Moreover it can be used not only for time series but also for any other real valued target variable. The algorithm implemented includes a Bayesian stochastic search methodology for model selection and a robust estimation based on bootstrapping. *rego* is fast because all the code is C++.
Installation notes
Compilation requires a C++11 compiler, lapack and blas installed. For a debian based SO, they can be easily installed running:
apt-get install build-essential liblapack-dev libblas-devInstallation on Windows requires Microsoft Visual C++ 14.0 or greater. (https://visualstudio.microsoft.com/it/downloads/)
Compiling and testing C++ code
sh compile/compile-cpp.sh test/test.cpp
/tmp/build/c++/test.oCompiling and testing Python code
Note! Only Python3 is supported!
pip install -r python/requirements.txt
sh compile/compile-py.sh
python test/test.py Compiling and testing R code
sh compile/compile-R.sh
Rscript BATCH test/test.RInstallation from PyPi
pip install --upgrade setuptools
pip install wheel
pip install Cython
pip install pandas
pip install regoInstallation from CRAN
R --vanilla -e 'install.packages(c("Rcpp", "RcppArmadillo"), repos="http://cran.us.r-project.org")'
R --vanilla -e 'install.packages(c("rego"), repos="http://cran.us.r-project.org")'Generating Python documentation
pip install sphinx
pip install numpydoc
pip install pygments --upgrade
pip install rinotype
cd python/src/cypack
python generate_doc.pyThe following .pdf will be generated:
python/src/cypack/docs/_build/rinoh/rego.pdf
