Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(fig.width = 7.15, fig.height = 4)
## ---- warning = FALSE, message = FALSE----------------------------------------
library(forecastML)
library(DT)
data("data_seatbelts", package = "forecastML")
data <- data_seatbelts
data <- data[, c("DriversKilled", "kms", "PetrolPrice", "law")]
DT::datatable(head(data, 5))
## -----------------------------------------------------------------------------
date_frequency <- "1 month"
dates <- seq(as.Date("1969-01-01"), as.Date("1984-12-01"), by = date_frequency)
## -----------------------------------------------------------------------------
horizons <- c(1, 6, 12) # forecasting 1, 1:6, and 1:12 months into the future.
# Create a list of length 3, one slot for each modeled forecast horizon.
lookback_control <- vector("list", length(horizons))
# Within each horizon-specific list, we'll identify the custom feature lags.
lookback_control <- lapply(lookback_control, function(x) {
list(
c(3, 12), # column 1: DriversKilled
1:3, # column 2: kms
1:12, # column 3: PetrolPrice
0 # column 4: law; this could be any value, dynamic features are set to '0' internally.
)
})
data_train <- forecastML::create_lagged_df(data, type = "train",
outcome_col = 1,
horizons = horizons,
lookback_control = lookback_control,
dates = dates,
frequency = date_frequency,
dynamic_features = "law")
## ---- results = 'hide'--------------------------------------------------------
plot(data_train)
## -----------------------------------------------------------------------------
horizons <- c(1, 6, 12) # forecasting 1, 1:6, and 1:12 months into the future.
# A list of length 3, one slot for each modeled forecast horizon.
lookback_control <- vector("list", length(horizons))
lookback_control <- lapply(lookback_control, function(x) {
# 12 feature lags for each of our 4 modeled features. Dynamic features will be coerced to "0" internally.
lapply(1:4, function(x) {1:12})
})
# Find the column index of the feature that we're removing.
remove_col <- which(grepl("PetrolPrice", names(data)))
# Remove the feature from the 12-month-out lagged data.frame.
lookback_control[[which(horizons == 12)]][remove_col] <- list(NULL)
data_train <- forecastML::create_lagged_df(data, type = "train",
outcome_col = 1,
lookback_control = lookback_control,
horizons = horizons,
dates = dates,
frequency = date_frequency,
dynamic_features = "law")
## ---- results = 'hide'--------------------------------------------------------
plot(data_train)[[remove_col]] # we're selecting 1 of our 3 lagged feature-level plots.
## -----------------------------------------------------------------------------
DT::datatable(head(data_train$horizon_12))
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.