prism_batch: PRISM stage 2 by batch

In PRISM.forecast: Penalized Regression with Inferred Seasonality Module - Forecasting Unemployment Initial Claims using 'Google Trends' Data

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.

PRISM.forecast documentation built on Oct. 23, 2020, 7:34 p.m.