prism {PRISM.forecast}

PRISM function

Description

A function for nowcasting and forecasting time series.

Usage

prism(
  data,
  data.early,
  GTdata,
  stl = TRUE,
  n.history = 700,
  n.training = 156,
  alpha = 1,
  UseGoogle = T,
  nPred.vec = 0:3,
  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).

data.early

historical time series of response variable before contemporaneous exogenous data, GTdata is available. (e.g., unemployment initial claim prior to 2004)

GTdata

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

stl

if TRUE, use STL seasonal decomposition; if FALSE, use classic additive seasonal decomposition.

n.history

training period for seasonal decomposition. (by default = 700 wks)

n.training

length of regression training period (by default = 156)

alpha

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

UseGoogle

boolean variable indicating whether to use Google Trend data.

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.

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 a vector of prediction with nPred.vec weeks forward.

Examples

prism_data = load_5y_search_data('0610')
data = prism_data$claim.data[1:200] # load claim data from 2006-01-07 to 2009-10-31
data[200] = NA # delete the data for the latest date and try to nowcast it.

data.early = prism_data$claim.earlyData # load claim prior to 2006
GTdata = prism_data$allSearch[1:200] # load Google trend data from 2006-01-07 to 2009-10-31

result = prism(data, data.early, GTdata) # call prism method
result$pred # output 0-3wk forward prediction