prism_batch {PRISM.forecast}

PRISM stage 2 by batch

Description

PRISM penalized linear regression function for a range of time (only used internally for back testing)

Usage

prism_batch(
  data,
  GTdata,
  var,
  n.training = 156,
  UseGoogle = T,
  alpha = 1,
  nPred.vec = 0:3,
  start.date = NULL,
  n.weeks = NULL,
  discount = 0.015,
  sepL1 = F
)

Arguments

data

time series of interest as xts, last element can be NA. (e.g., unemployment initial claim data in the same period as GTdata).

GTdata

contemporaneous exogenous data as xts. (e.g., Google Trend data)

var

generated regressors from stage 1.

n.training

length of regression training period (by default = 156)

UseGoogle

boolean variable indicating whether to use Google Trend data.

alpha

penalty between lasso and ridge. alpha=1 represents lasso, alpha=0 represents ridge, alpha=NA represents no penalty.

nPred.vec

the number of periods ahead for forecast. nPred.vec could be a vector of intergers. e.g. nPred.vec=0:3 gives results from nowcast to 3-week ahead forecast.

start.date

the starting date for forecast. If NULL, the forecast start at the earliest possible date.

n.weeks

the number of weeks in the batch. If NULL, the forecast end at the latest possible date.

discount

exponential weighting: (1-discount)^lag (by default = 0.015)

sepL1

if TRUE, use separate L1 regularization parameters for time series components and exogenous variables (Goolgle Trend data)

Value

A list of following named objects

• coef coefficients for Intercept, z.lags, seasonal.lags and exogenous variables.

• pred prediction results for n.weeks from start.date.