prism: PRISM function

In ryanddyi/prism: Penalized Regression with Inferred Seasonality Module - Forecasting Unemployment Initial Claims using 'Google Trends' Data

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 = 0.01, 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 = 0.01).
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

ryanddyi/prism documentation built on Oct. 25, 2020, 11:25 a.m.