Extending mlr3 to time series forecasting.
This package is in an early stage of development and should be considered experimental. If you are interested in experimenting with it, we welcome your feedback!
Installation
Install the development version from GitHub:
# install.packages("pak")
pak::pak("mlr-org/mlr3forecast")Usage
The goal of mlr3forecast is to extend mlr3 to time series forecasting. This is achieved by introducing new classes and methods for forecasting tasks, learners, and resamplers. For now the forecasting task and learner is restricted to time series regression tasks, but might be extended to classification tasks in the future.
We have two goals, one to support traditional forecasting learners and the other to support machine learning forecasting, i.e. using regression learners and applying them to forecasting tasks. The design of the latter is still in flux and may change.
Example: forecasting with forecast learner
Currently, we support native forecasting learners from the forecast and smooth packages. In the future, we plan to support more learners.
library(mlr3forecast)
library(mlr3pipelines)
task = tsk("airpassengers")
task
#>
#> ── <TaskFcst> (144x1): Monthly Airline Passenger Numbers 1949-1960 ─────────────
#> • Target: passengers
#> • Properties: ordered
#> • Order by: month
#> • Frequency: monthly
# or plot the task
autoplot(task)
# train a forecast learner
learner = lrn("fcst.auto_arima")$train(task)
prediction = learner$predict(task, 140:144)
prediction
#>
#> ── <PredictionRegr> for 5 observations: ────────────────────────────────────────
#> row_ids truth response month
#> 140 606 623.9219 1960-08-01
#> 141 508 513.8585 1960-09-01
#> 142 461 450.7762 1960-10-01
#> 143 390 410.8961 1960-11-01
#> 144 432 439.9462 1960-12-01
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 13.85518
# generate new data to forecast unseen data
newdata = generate_newdata(task, 12L)
head(newdata)
#> month passengers
#> 1: 1961-01-01 NA
#> 2: 1961-02-01 NA
#> 3: 1961-03-01 NA
#> 4: 1961-04-01 NA
#> 5: 1961-05-01 NA
#> 6: 1961-06-01 NA
prediction = learner$predict_newdata(newdata, task)
prediction
#>
#> ── <PredictionRegr> for 12 observations: ───────────────────────────────────────
#> row_ids truth response month
#> 1 NA 445.6351 1961-01-01
#> 2 NA 420.3953 1961-02-01
#> 3 NA 449.1988 1961-03-01
#> --- --- --- ---
#> 10 NA 494.1275 1961-10-01
#> 11 NA 423.3336 1961-11-01
#> 12 NA 465.5085 1961-12-01
# add a target log transformation
learner = as_learner(ppl(
"targettrafo",
graph = lrn("fcst.auto_arima"),
targetmutate.trafo = function(x) log(x),
targetmutate.inverter = function(x) list(response = exp(x$response))
))
prediction = learner$train(task)$predict(task, 140:144)
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 12.29896
# works with quantile response
learner = lrn(
"fcst.auto_arima",
predict_type = "quantiles",
quantiles = c(0.1, 0.15, 0.5, 0.85, 0.9),
quantile_response = 0.5
)$train(task)
learner$predict_newdata(newdata, task)
#>
#> ── <PredictionRegr> for 12 observations: ───────────────────────────────────────
#> row_ids truth q0.1 q0.15 q0.5 q0.85 q0.9 response month
#> 1 NA 430.8905 433.7106 445.6351 457.5595 460.3796 445.6351 1961-01-01
#> 2 NA 403.0907 406.4005 420.3953 434.3901 437.6999 420.3953 1961-02-01
#> 3 NA 429.7726 433.4882 449.1988 464.9093 468.6249 449.1988 1961-03-01
#> --- --- --- --- --- --- --- --- ---
#> 10 NA 469.8626 474.5036 494.1275 513.7514 518.3925 494.1275 1961-10-01
#> 11 NA 398.8383 403.5234 423.3336 443.1438 447.8290 423.3336 1961-11-01
#> 12 NA 440.8230 445.5445 465.5085 485.4725 490.1940 465.5085 1961-12-01
# resampling
learner = lrn("fcst.auto_arima")
resampling = rsmp("fcst.holdout", ratio = 0.7)
rr = resample(task, learner, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 27.1211Example: forecasting with regression learner
library(mlr3learners)
task = tsk("airpassengers")
learner = lrn("regr.ranger")
flrn = as_learner_fcst(learner, lags = 1:12)$train(task)
newdata = generate_newdata(task, 12L)
prediction = flrn$predict_newdata(newdata, task)
prediction
#>
#> ── <PredictionRegr> for 12 observations: ───────────────────────────────────────
#> row_ids truth response month
#> 1 NA 435.5233 1961-01-01
#> 2 NA 435.4838 1961-02-01
#> 3 NA 455.6493 1961-03-01
#> --- --- --- ---
#> 10 NA 477.9975 1961-10-01
#> 11 NA 439.3823 1961-11-01
#> 12 NA 436.3503 1961-12-01
prediction = flrn$predict(task, 140:144)
prediction
#>
#> ── <PredictionRegr> for 5 observations: ────────────────────────────────────────
#> row_ids truth response month
#> 140 606 574.9785 1960-08-01
#> 141 508 501.7538 1960-09-01
#> 142 461 453.4319 1960-10-01
#> 143 390 414.4810 1960-11-01
#> 144 432 431.5647 1960-12-01
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 18.21064
flrn = as_learner_fcst(learner, lags = 1:12)
resampling = rsmp("fcst.holdout", ratio = 0.9)
rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 47.51994
resampling = rsmp("fcst.cv")
rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 27.05096Or with some feature engineering using mlr3pipelines:
library(mlr3pipelines)
graph = po(
"datefeatures",
param_vals = list(
week_of_year = FALSE,
day_of_year = FALSE,
day_of_month = FALSE,
day_of_week = FALSE
)
)
task = tsk("airpassengers")
task$set_col_roles("month", add = "feature")
flrn = as_learner_fcst(lrn("regr.ranger"), lags = 1:12)
glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 13.97363Example: forecasting electricity demand
library(mlr3learners)
library(mlr3pipelines)
task = tsk("electricity")
task$set_col_roles("date", add = "feature")
graph = po("datefeatures", param_vals = list(year = FALSE))
flrn = as_learner_fcst(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% flrn)$train(task)
max_date = task$data()[.N, date]
newdata = data.table(
date = max_date + 1:14,
demand = rep(NA_real_, 14L),
temperature = 26,
holiday = c(TRUE, rep(FALSE, 13L))
)
prediction = glrn$predict_newdata(newdata, task)
prediction
#>
#> ── <PredictionRegr> for 14 observations: ───────────────────────────────────────
#> row_ids truth response date
#> 1 NA 188559.2 2015-01-01
#> 2 NA 197294.4 2015-01-02
#> 3 NA 190284.8 2015-01-03
#> --- --- --- ---
#> 12 NA 222640.0 2015-01-12
#> 13 NA 227496.0 2015-01-13
#> 14 NA 228514.9 2015-01-14Example: global forecasting
library(mlr3learners)
library(mlr3pipelines)
library(tsibble)
dt = setDT(tsibbledata::aus_livestock)
setnames(dt, tolower)
dt[, month := as.Date(month)]
dt = dt[, .(count = sum(count)), by = .(state, month)]
setorder(dt, state, month)
task = as_task_fcst(dt, id = "aus_livestock", target = "count", order = "month", key = "state", freq = "monthly")
task$set_col_roles("month", add = "feature")
graph = po(
"datefeatures",
param_vals = list(
week_of_year = FALSE,
day_of_week = FALSE,
day_of_month = FALSE,
day_of_year = FALSE
)
)
flrn = as_learner_fcst(lrn("regr.ranger"), 1:12)
glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 4460:4464)
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 27124.47
resampling = rsmp("fcst.holdout", ratio = 0.9)
rr = resample(task, glrn, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 109705.2Example: global vs local forecasting
In machine learning forecasting the difference between forecasting a time series and longitudinal data is often referred to local and global forecasting.
retail = setDT(tsibbledata::aus_retail)
setnames(retail, tolower)
retail[, month := as.Date(month)]
vic = retail[state == "Victoria"]
vic[, let(state = NULL, `series id` = NULL)]
vic_train = vic[month < as.Date("2015-01-01")]
vic_test = vic[month >= as.Date("2015-01-01")]
# global forecasting
task_train = as_task_fcst(
vic_train,
id = "aus_retail_vic",
target = "turnover",
order = "month",
key = "industry",
freq = "monthly"
)
task_test = as_task_fcst(
vic_test,
id = "aus_retail_vic",
target = "turnover",
order = "month",
key = "industry",
freq = "monthly"
)
learner = lrn("regr.ranger")
flrn = as_learner_fcst(learner, lags = 1:12)$train(task_train)
prediction_global = flrn$predict(task_test)
prediction_global
prediction_global$score(msr("regr.rmse"))
# local forecasting
prediction_local = map(split(vic, by = "industry", drop = TRUE), function(dt) {
task_train = as_task_fcst(
dt[month < as.Date("2015-01-01")],
id = "aus_retail_vic_local",
target = "turnover",
order = "month",
freq = "monthly"
)
task_test = as_task_fcst(
dt[month >= as.Date("2015-01-01")],
id = "aus_retail_vic_local",
target = "turnover",
order = "month",
freq = "monthly"
)
flrn = as_learner_fcst(learner, lags = 1:12)$train(task_train)
prediction = flrn$predict(task_test)
prediction
})
do.call(c, prediction_local)$score(msr("regr.rmse"))Example: Custom PipeOps
library(mlr3learners)
library(mlr3pipelines)
task = tsk("airpassengers")
pop = po("fcst.lags", lags = 1:12)
new_task = pop$train(list(task))[[1L]]
new_task$data()
task = tsk("airpassengers")
graph = po("fcst.lags", lags = 1:12) %>>%
po(
"datefeatures",
param_vals = list(
week_of_year = FALSE,
day_of_week = FALSE,
day_of_month = FALSE,
day_of_year = FALSE
)
)
flrn = ForecastLearnerManual$new(lrn("regr.ranger"))
glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 1:12)
prediction$score(msr("regr.rmse"))
newdata = generate_newdata(task, 12L)
glrn$predict_newdata(newdata, task)
graph = po("fcst.lags", lags = 1:12) %>>%
po(
"datefeatures",
param_vals = list(
week_of_year = FALSE,
day_of_week = FALSE,
day_of_month = FALSE,
day_of_year = FALSE
)
)
task = tsk("airpassengers")
flrn = ForecastLearnerManual$new(lrn("regr.ranger"))
glrn = as_learner(graph %>>% flrn)
trafo = po("fcst.targetdiff", lags = 12L)
pipeline = ppl("targettrafo", graph = glrn, trafo_pipeop = trafo)
glrn = as_learner(pipeline)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(msr("regr.rmse"))