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!
Install the development version from GitHub:
# install.packages("pak")
pak::pak("mlr-org/mlr3forecast")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.
Currently, we support native forecasting learners from the forecast and smooth packages. In the future, we plan to support more learners.
library(mlr3forecast)
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)
learner = lrn("fcst.auto_arima")$train(task)
prediction = learner$predict(task, 140:144)
prediction
#>
#> ── <PredictionRegr> for 5 observations: ────────────────────────────────────────
#> row_ids truth response
#> 140 606 623.9219
#> 141 508 513.8585
#> 142 461 450.7762
#> 143 390 410.8961
#> 144 432 439.9462
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 13.85518
# currently there is no specialized prediction object
# for now you can do the following to add the order column to the prediction
tab = as.data.table(prediction)
tab = tab[task$order, on = .(row_ids = row_id), nomatch = NULL]
setcolorder(tab, c("row_ids", "order"))
tab
#> row_ids order truth response
#> 1: 140 1960-08-01 606 623.9219
#> 2: 141 1960-09-01 508 513.8585
#> 3: 142 1960-10-01 461 450.7762
#> 4: 143 1960-11-01 390 410.8961
#> 5: 144 1960-12-01 432 439.9462
# generate new data to forecast unseen data
newdata = generate_newdata(task, 12L)
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
#> 7: 1961-07-01 NA
#> 8: 1961-08-01 NA
#> 9: 1961-09-01 NA
#> 10: 1961-10-01 NA
#> 11: 1961-11-01 NA
#> 12: 1961-12-01 NA
prediction = learner$predict_newdata(newdata, task)
prediction
#>
#> ── <PredictionRegr> for 12 observations: ───────────────────────────────────────
#> row_ids truth response
#> 1 NA 445.6351
#> 2 NA 420.3953
#> 3 NA 449.1988
#> --- --- ---
#> 10 NA 494.1275
#> 11 NA 423.3336
#> 12 NA 465.5085
# add the order column to the prediction
tab = as.data.table(prediction)
tab[, order := newdata[, month]]
setcolorder(tab, c("row_ids", "order"))
tab
#> row_ids order truth response
#> 1: 1 1961-01-01 NA 445.6351
#> 2: 2 1961-02-01 NA 420.3953
#> 3: 3 1961-03-01 NA 449.1988
#> 4: 4 1961-04-01 NA 491.8405
#> 5: 5 1961-05-01 NA 503.3951
#> 6: 6 1961-06-01 NA 566.8632
#> 7: 7 1961-07-01 NA 654.2610
#> 8: 8 1961-08-01 NA 638.5983
#> 9: 9 1961-09-01 NA 540.8846
#> 10: 10 1961-10-01 NA 494.1275
#> 11: 11 1961-11-01 NA 423.3336
#> 12: 12 1961-12-01 NA 465.5085
# works with quantile response
learner = lrn(
"fcst.auto_arima",
predict_type = "quantiles",
quantiles = c(0.1, 0.15, 0.5, 0.85, 0.9),
quantile_response = 0.5
)$train(task)
learner$predict_newdata(newdata, task)
#>
#> ── <PredictionRegr> for 12 observations: ───────────────────────────────────────
#> row_ids truth q0.1 q0.15 q0.5 q0.85 q0.9 response
#> 1 NA 430.8905 433.7106 445.6351 457.5595 460.3796 445.6351
#> 2 NA 403.0907 406.4005 420.3953 434.3901 437.6999 420.3953
#> 3 NA 429.7726 433.4882 449.1988 464.9093 468.6249 449.1988
#> --- --- --- --- --- --- --- ---
#> 10 NA 469.8626 474.5036 494.1275 513.7514 518.3925 494.1275
#> 11 NA 398.8383 403.5234 423.3336 443.1438 447.8290 423.3336
#> 12 NA 440.8230 445.5445 465.5085 485.4725 490.1940 465.5085library(mlr3learners)
task = tsk("airpassengers")
learner = lrn("regr.ranger")
flrn = ForecastLearner$new(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
#> 1 NA 435.2335
#> 2 NA 435.1569
#> 3 NA 456.3607
#> --- --- ---
#> 10 NA 474.1740
#> 11 NA 441.7171
#> 12 NA 441.8077
prediction = flrn$predict(task, 140:144)
prediction
#>
#> ── <PredictionRegr> for 5 observations: ────────────────────────────────────────
#> row_ids truth response
#> 1 606 576.4443
#> 2 508 500.0087
#> 3 461 455.8599
#> 4 390 413.7775
#> 5 432 429.3613
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 17.528
flrn = ForecastLearner$new(learner, lags = 1:12)
resampling = rsmp("fcst.holdout", ratio = 0.9)
rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 47.90481
resampling = rsmp("fcst.cv")
rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 26.87853Or with some feature engineering using mlr3pipelines:
library(mlr3pipelines)
graph = ppl("convert_types", "Date", "POSIXct") %>>%
po(
"datefeatures",
param_vals = list(
week_of_year = FALSE,
day_of_year = FALSE,
day_of_month = FALSE,
day_of_week = FALSE,
is_day = FALSE,
hour = FALSE,
minute = FALSE,
second = FALSE
)
)
task = tsk("airpassengers")
task$set_col_roles("month", add = "feature")
flrn = ForecastLearner$new(lrn("regr.ranger"), lags = 1:12)
glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 14.5506library(mlr3learners)
library(mlr3pipelines)
task = tsk("electricity")
task$set_col_roles("date", add = "feature")
graph = ppl("convert_types", "Date", "POSIXct") %>>%
po(
"datefeatures",
param_vals = list(
year = FALSE,
is_day = FALSE,
hour = FALSE,
minute = FALSE,
second = FALSE
)
)
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
glrn = as_learner(graph %>>% flrn)$train(task)
max_date = task$data()[.N, date]
newdata = data.frame(
date = max_date + 1:14,
demand = rep(NA_real_, 14L),
temperature = 26,
holiday = c(TRUE, rep(FALSE, 13L))
)
prediction = glrn$predict_newdata(newdata, task)
prediction
#>
#> ── <PredictionRegr> for 14 observations: ───────────────────────────────────────
#> row_ids truth response
#> 1 NA 187309.4
#> 2 NA 198552.8
#> 3 NA 188609.2
#> --- --- ---
#> 12 NA 220803.9
#> 13 NA 223494.1
#> 14 NA 225832.0library(mlr3learners)
library(mlr3pipelines)
library(tsibble)
task = tsibbledata::aus_livestock |>
as.data.table() |>
setnames(tolower) |>
_[, month := as.Date(month)] |>
_[, .(count = sum(count)), by = .(state, month)] |>
setorder(state, month) |>
as_task_fcst(
id = "aus_livestock",
target = "count",
order = "month",
key = "state",
freq = "monthly"
)
task$set_col_roles("month", add = "feature")
graph = ppl("convert_types", "Date", "POSIXct") %>>%
po(
"datefeatures",
param_vals = list(
week_of_year = FALSE,
day_of_week = FALSE,
day_of_month = FALSE,
day_of_year = FALSE,
is_day = FALSE,
hour = FALSE,
minute = FALSE,
second = FALSE
)
)
task = graph$train(task)[[1L]]
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)$train(task)
prediction = flrn$predict(task, 4460:4464)
prediction$score(msr("regr.rmse"))
#> regr.rmse
#> 23878.64
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
resampling = rsmp("fcst.holdout", ratio = 0.9)
rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
#> regr.rmse
#> 30134.58In machine learning forecasting the difference between forecasting a time series and longitudinal data is often refered to local and global forecasting.
# TODO: find better task example, since the effect is minor here
graph = ppl("convert_types", "Date", "POSIXct") %>>%
po(
"datefeatures",
param_vals = list(
week_of_year = FALSE,
day_of_week = FALSE,
day_of_month = FALSE,
day_of_year = FALSE,
is_day = FALSE,
hour = FALSE,
minute = FALSE,
second = FALSE
)
)
# local forecasting
task = tsibbledata::aus_livestock |>
as.data.table() |>
setnames(tolower) |>
_[, month := as.Date(month)] |>
_[state == "Western Australia", .(count = sum(count)), by = .(month)] |>
setorder(month) |>
as_task_fcst(id = "aus_livestock", target = "count", order = "month")
task = graph$train(task)[[1L]]
flrn = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
dt = task$backend$data(
rows = task$row_ids,
cols = c(task$backend$primary_key, "month.year")
)
setnames(dt, c("row_id", "year"))
row_ids = dt[year >= 2015, row_id]
prediction = flrn$predict(task, row_ids)
prediction$score(msr("regr.rmse"))
# global forecasting
task = tsibbledata::aus_livestock |>
as.data.table() |>
setnames(tolower) |>
_[, month := as.Date(month)] |>
_[, .(count = sum(count)), by = .(state, month)] |>
setorder(state, month) |>
as_task_fcst(id = "aus_livestock", target = "count", order = "month", key = "state")
task = graph$train(task)[[1L]]
task$col_roles$key = "state"
flrn = ForecastLearner$new(lrn("regr.ranger"), 1L)$train(task)
dt = task$backend$data(
rows = task$row_ids,
cols = c(task$backend$primary_key, "month.year", "state")
)
setnames(dt, c("row_id", "year", "state"))
row_ids = dt[year >= 2015 & state == "Western Australia", row_id]
prediction = flrn$predict(task, row_ids)
prediction$score(msr("regr.rmse"))library(mlr3learners)
library(mlr3pipelines)
task = tsk("airpassengers")
pop = po("fcst.lag", lags = 1:12)
new_task = pop$train(list(task))[[1L]]
new_task$data()
task = tsk("airpassengers")
graph = po("fcst.lag", lags = 1:12) %>>%
ppl("convert_types", "Date", "POSIXct") %>>%
po(
"datefeatures",
param_vals = list(
week_of_year = FALSE,
day_of_week = FALSE,
day_of_month = FALSE,
day_of_year = FALSE,
is_day = FALSE,
hour = FALSE,
minute = FALSE,
second = FALSE
)
)
flrn = ForecastLearnerManual$new(lrn("regr.ranger"))
glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(msr("regr.rmse"))
newdata = generate_newdata(task, 12L)
glrn$predict_newdata(newdata, task)Some common target transformations in forecasting are:
- differencing (WIP)
- log transformation, see example below
- power transformations such as Box-Cox and Yeo-Johnson currently only supported as feature transformation and not target
- scaling/normalization, available see here
trafo = po(
"targetmutate",
param_vals = list(
trafo = function(x) log(x),
inverter = function(x) list(response = exp(x$response))
)
)
graph = po("fcst.lag", lags = 1:12) %>>%
ppl("convert_types", "Date", "POSIXct") %>>%
po(
"datefeatures",
param_vals = list(
week_of_year = FALSE,
day_of_week = FALSE,
day_of_month = FALSE,
day_of_year = FALSE,
is_day = FALSE,
hour = FALSE,
minute = FALSE,
second = FALSE
)
)
task = tsk("airpassengers")
flrn = ForecastLearnerManual$new(lrn("regr.ranger"))
glrn = as_learner(graph %>>% flrn)
pipeline = ppl("targettrafo", graph = glrn, trafo_pipeop = trafo)
glrn = as_learner(pipeline)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(msr("regr.rmse"))graph = po("fcst.lag", lags = 1:12) %>>%
ppl("convert_types", "Date", "POSIXct") %>>%
po(
"datefeatures",
param_vals = list(
week_of_year = FALSE,
day_of_week = FALSE,
day_of_month = FALSE,
day_of_year = FALSE,
is_day = FALSE,
hour = FALSE,
minute = FALSE,
second = FALSE
)
)
task = tsk("airpassengers")
flrn = ForecastLearnerManual$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"))
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