| states_weighted_forc {lmForc} | R Documentation |
States weighted forecast
Description
states_weighted_forc takes two or more forecasts, a data frame, matrix,
or array of matching variables, an optional vector of time data associated with
the matching variables, a matching window size, a matching function, and an
error function. For each forecast period, matching_vars are standardized
and the current state of the world is set as the the past matching_window
periods of the matching variables. The current state is compared to all past
periods of the matching variables using the matching function. The current state
is matched to the past state that minimizes the matching function. The forecast
error function is then used to compute the accuracy of each forecast over the matched
past state. Forecast weights are computed based on this forecast accuracy,
and the current period forecast is subsequently computed based on the forecast
weights. Produces a weighted average of multiple forecasts based on how each
forecast performed during the past state that is most similar to the current
state of the world.
Usage
states_weighted_forc(
...,
matching_vars,
time_vec = NULL,
matching_window,
matching = "euclidean",
errors = "mse",
return_weights = FALSE
)
Arguments
... |
Two or more forecasts of class Forecast. |
matching_vars |
data frame, array, or matrix of variables used to match the current state of the world to a past state. |
time_vec |
Vector of any class that is equal in length to the data
in |
matching_window |
Integer representing the window size over which the
current state of the world is matched to a past state. Forecasts are also
weighted based on their accuracy over |
matching |
Character, "euclidean", "mse", or "rmse". Selects the function used to match the current state of the world to a past state. |
errors |
Character, either "mse" or "rmse". Selects whether forecast accuracy is evaluated using mean squared errors or root mean squared errors. |
return_weights |
Boolean, selects whether the weights used to weight forecasts in each period are returned. If TRUE, a data frame of weights and matched periods is returned to the Global Environment. |
Details
Forecasts are weighted in each period with the function below. The error function used is MSE or RMSE depending on user selection. This example shows MSE errors.
weight = (1 / MSE(forecast)) / (1 / sum(MSE(forecasts)))
Value
Forecast object that contains the state weighted forecast.
See Also
For a detailed example see the help vignette:
vignette("lmForc", package = "lmForc")
Examples
date <- as.Date(c("2010-03-31", "2010-06-30", "2010-09-30", "2010-12-31",
"2011-03-31", "2011-06-30", "2011-09-30", "2011-12-31",
"2012-03-31", "2012-06-30"))
future <- as.Date(c("2011-03-31", "2011-06-30", "2011-09-30", "2011-12-31",
"2012-03-31", "2012-06-30", "2012-09-30", "2012-12-31",
"2013-03-31", "2013-06-30"))
y <- c(1.09, 1.71, 1.09, 2.46, 1.78, 1.35, 2.89, 2.11, 2.97, 0.99)
x1 <- c(4.22, 3.86, 4.27, 5.60, 5.11, 4.31, 4.92, 5.80, 6.30, 4.17)
x2 <- c(10.03, 10.49, 10.85, 10.47, 9.09, 10.91, 8.68, 9.91, 7.87, 6.63)
data <- data.frame(date, y, x1, x2)
matching_vars <- data[, c("x1", "x2")]
y1_forecast <- Forecast(
origin = date,
future = future,
forecast = c(1.33, 1.36, 1.38, 1.68, 1.60, 1.55, 1.32, 1.22, 1.08, 0.88),
realized = c(1.78, 1.35, 2.89, 2.11, 2.97, 0.99, 1.31, 1.41, 1.02, 1.05),
h_ahead = 4L
)
y2_forecast <- Forecast(
origin = date,
future = future,
forecast = c(0.70, 0.88, 1.03, 1.05, 1.01, 0.82, 0.95, 1.09, 1.07, 1.06),
realized = c(1.78, 1.35, 2.89, 2.11, 2.97, 0.99, 1.31, 1.41, 1.02, 1.05),
h_ahead = 4L
)
states_weighted_forc(
y1_forecast, y2_forecast,
matching_vars = matching_vars,
time_vec = data$date,
matching_window = 2L,
matching = "euclidean",
errors = "mse",
return_weights = FALSE
)
states_weighted_forc(
y1_forecast, y2_forecast,
matching_vars = matching_vars,
time_vec = data$date,
matching_window = 3L,
matching = "rmse",
errors = "rmse"
)