Description Usage Arguments Value
PRISM penalized linear regression function for a range of time (only used internally for back testing)
1 2 3 | prism_batch(data, GTdata, var, n.training = 156, UseGoogle = T, alpha = 1,
nPred.vec = 0:3, start.date = NULL, n.weeks = NULL, discount = 0.01,
sepL1 = F)
|
data |
time series of interest as xts, last element can be NA. (e.g., unemployment initial claim data in the same period as |
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 = 0.01) |
sepL1 |
if TRUE, use separate L1 regularization parameters for time series components and exogenous variables (Goolgle Trend data) |
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
.
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