back_test: Out-of-sample prediction for whole period

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

Description

Out-of-sample prediction for whole period

Usage

back_test(
  n.lag = 1:52,
  s.window = 52,
  n.history = 700,
  stl = TRUE,
  n.training = 156,
  UseGoogle = T,
  alpha = 1,
  nPred = 0,
  discount = 0.015,
  sepL1 = F
)

Arguments

n.lag	the number of lags to be used as regressor in Stage 2 of PRISM (by default = 1:52 for weekly data)
s.window	seasonality span in seasonal decomposition (by default = 52 for weekly data)
n.history	length of training period (e.g. in weeks) for seasonal decomposition.
stl	if TRUE, use STL seasonal decomposition; if FALSE, use classic additive seasonal decomposition.
n.training	length of training period in Stage 2, penalized linear regression (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 (by default alpha = 1).
nPred	the number of periods ahead for forecast. nPred = 0,1,2,3.
discount	exponential weighting: (1-discount)^lag.
sepL1	if TRUE, use separate L1 regularization parameters for time series components and exogenous variables (Goolgle Trend data)

Value

prediction nPred week ahead prediction of the whole periods (07 - 20).

Examples

claim_data = load_claim_data()

# It may take a few minutes.
prism_prediction = back_test()
# evaluate the out-of-sample prediction error as a ratio to naive method
evaluation_table(claim_data, prism_prediction)

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