fastTS {fastTS}

Fast time series modeling with ranked sparsity

Description

Uses penalized regression to quickly fit time series models with potentially complex seasonal patterns and exogenous variables. Based on methods described in Peterson & Cavanaugh (2024).

Usage

fastTS(
  y,
  X = NULL,
  n_lags_max,
  gamma = c(0, 2^(-2:4)),
  ptrain = 0.8,
  pf_eps = 0.01,
  w_endo,
  w_exo,
  weight_type = c("pacf", "parametric"),
  m = NULL,
  r = c(rep(0.1, length(m)), 0.01),
  plot = FALSE,
  ncvreg_args = list(penalty = "lasso", returnX = FALSE, lambda.min = 0.001)
)

## S3 method for class 'fastTS'
plot(x, log.l = TRUE, ...)

## S3 method for class 'fastTS'
coef(object, choose = c("AICc", "BIC"), ...)

## S3 method for class 'fastTS'
print(x, ...)

## S3 method for class 'fastTS'
summary(object, choose = c("AICc", "BIC"), ...)

Arguments

y

univariate time series outcome

X

matrix of predictors (no intercept)

n_lags_max

maximum number of lags to consider

gamma

vector of exponent for weights

ptrain

prop. to leave out for test data

pf_eps

penalty factors below this will be set to zero

w_endo

optional pre-specified weights for endogenous terms

w_exo

optional pre-specified weights for exogenous terms (details)

weight_type

type of weights to use for endogenous terms

m

mode(s) for seasonal lags (used if weight_type = "parametric")

r

penalty factors for seasonal + local scaling functions (used if weight_type = "parametric")

plot

logical; whether to plot the penalty functions

ncvreg_args

additional args to pass through to ncvreg

x

a fastTS object

log.l

Should the x-axis (lambda) be logged?

...

passed to downstream functions

object

a fastTS object

choose

which criterion to use for lambda selection (AICc or BIC)

Details

The default weights for exogenous features will be chosen based on a similar approach to the adaptive lasso (using bivariate OLS estimates). For lower dimensional X, it's advised to set w_exo="unpenalized", because this allows for statistical inference on exogenous variable coefficients via the summary function.

By default, a seasonal frequency m must not be specified and the PACF is used to estimate the weights for endogenous terms. A parametric version is also available, which allows for a penalty scaling function that penalizes seasonal and recent lags less according to the penalty scaling functions described in Peterson & Cavanaugh (2024). See the penalty_scaler function for more details, and to plot the penalty function for various values of m and r.

Value

A list of class fastTS with elements

fits

a list of lasso fits

ncvreg_args

arguments passed to ncvreg

gamma

the (negative) exponent on the penalty weights, one for each fit

n_lags_max

the maximum number of lags

y

the time series

X

the utilized matrix of exogenous features

oos_results

results on test data using best of fits

train_idx

index of observations used in training data

weight_type

the type of weights used for endogenous terms

m

the mode(s) for seasonal lags (used if weight_type = "parametric")

r

penalty factors for seasonal + local scaling functions

ptrain

the proportion used to train the model

x invisibly

a vector of model coefficients

x (invisibly)

the summary object produced by ncvreg evaluated at the best tuning parameter combination (best AICc).

References

Breheny, P. and Huang, J. (2011) Coordinate descent algorithms for nonconvex penalized regression, with applications to biological feature selection. Ann. Appl. Statist., 5: 232-253.

Peterson, R.A., Cavanaugh, J.E. (2022) Ranked sparsity: a cogent regularization framework for selecting and estimating feature interactions and polynomials. AStA Adv Stat Anal. https://doi.org/10.1007/s10182-021-00431-7

Peterson, R.A., Cavanaugh, J.E. (2024). Fast, effective, and coherent time series modeling using the sparsity-ranked lasso. Statistical Modelling (accepted). DOI: https://doi.org/10.48550/arXiv.2211.01492

See Also

predict.fastTS

Examples

data("LakeHuron")
fit_LH <- fastTS(LakeHuron)
fit_LH
coef(fit_LH)
plot(fit_LH)