Dynamic Mode Decomposition for Multivariate Time Feature
Prediction
An application of Dynamic Mode Decomposition for prediction of time features. Automatic search for the best model across the space of all possible feature combinations and ranks of Singular Value Decomposition.

Time series regression.  The dyn class interfaces
ts, irts, its, zoo and zooreg time series classes to
lm, glm, loess, quantreg::rq, MASS::rlm, quantreg::rq,
randomForest::randomForest and other regression
functions allowing those functions to be used with
time series including specifications that may contain
lags, diffs and missing values.
its, MASS, quantreg (>= 3.82), randomForest, tseries

its (>= 1.0.9), MASS, quantreg (>= 3.82), randomForest, tseries

Time series regression.  The dyn class interfaces ts,
irts, its, zoo and zooreg time series classes to lm, glm,
loess, quantreg::rq, MASS::rlm, MCMCpack::MCMCregress,
quantreg::rq, randomForest::randomForest and other regression
functions allowing those functions to be used with time series
including specifications that may contain lags, diffs and
missing values.
its (>= 1.0.9), lattice, MASS, MCMCpack, quantreg (>= 3.82),
randomForest, sandwich, tseries

Time series regression.  The dyn class interfaces
ts, irts, its, zoo and zooreg time series classes to
lm, glm, loess, quantreg::rq, MASS::rlm, MCMCpack::MCMCregress,
quantreg::rq, randomForest::randomForest and other regression
functions allowing those functions to be used with
time series including specifications that may contain
lags, diffs and missing values.
its (>= 1.0.9), MASS, MCMCpack, quantreg (>= 3.82), randomForest,
sandwich, tseries

Time series regression.  The dyn class interfaces ts,
irts(), its, zoo() and zooreg() time series classes to lm(), glm(),
loess(), quantreg::rq(), MASS::rlm(), MCMCpack::MCMCregress(),
quantreg::rq(), randomForest::randomForest() and other regression
functions allowing those functions to be used with time series
including specifications that may contain lags, diffs and
missing values.