README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

lgr::get_logger("mlr3")$set_threshold("warn")
options(datatable.print.class = FALSE, datatable.print.keys = FALSE)
library(data.table)
library(mlr3misc)
```


# mlr3forecast

Extending mlr3 to time series forecasting.

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> 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](https://github.com/):

```{r, eval = FALSE}
# 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 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 package.
In the future, we plan to support more forecasting learners.

```{r, message = FALSE}
library(mlr3forecast)

task = tsk("airpassengers")
learner = lrn("fcst.auto_arima")$train(task)
prediction = learner$predict(task, 140:144)
prediction$score(msr("regr.rmse"))
newdata = generate_newdata(task, 12L)
learner$predict_newdata(newdata, task)

# 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)
```

### Example: forecasting with regression learner

```{r, message = FALSE}
library(mlr3learners)

task = tsk("airpassengers")
# we have to remove the date feature for regression learners
task$select(setdiff(task$feature_names, "date"))
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:12)$train(task)
newdata = data.frame(passengers = rep(NA_real_, 3L))
prediction = flrn$predict_newdata(newdata, task)
prediction
prediction = flrn$predict(task, 142:144)
prediction
prediction$score(msr("regr.rmse"))

flrn = ForecastLearner$new(lrn("regr.ranger"), 1:12)
resampling = rsmp("forecast_holdout", ratio = 0.9)
rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))

resampling = rsmp("forecast_cv")
rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
```

Or with some feature engineering using mlr3pipelines:

```{r}
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")
flrn = ForecastLearner$new(lrn("regr.ranger"), 1:12)
glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(msr("regr.rmse"))
```

### Example: forecasting electricity demand

```{r, message = FALSE}
library(mlr3learners)
library(mlr3pipelines)

task = tsk("electricity")
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
```

### Example: global forecasting (longitudinal data)

```{r, message = FALSE}
library(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"
  )

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"))

flrn = ForecastLearner$new(lrn("regr.ranger"), 1:3)
resampling = rsmp("forecast_holdout", ratio = 0.9)
rr = resample(task, flrn, resampling)
rr$aggregate(msr("regr.rmse"))
```

### Example: global vs local forecasting

In machine learning forecasting the difference between forecasting a time series
and longitudinal data is often refered to local and global forecasting.

```{r, eval = FALSE}
# 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)$train(task)
tab = task$backend$data(
  rows = task$row_ids, cols = c(task$backend$primary_key, "month.year")
)
setnames(tab, c("row_id", "year"))
row_ids = tab[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)$train(task)
tab = task$backend$data(
  rows = task$row_ids, cols = c(task$backend$primary_key, "month.year", "state")
)
setnames(tab, c("row_id", "year", "state"))
row_ids = tab[year >= 2015 & state == "Western Australia", row_id]
prediction = flrn$predict(task, row_ids)
prediction$score(msr("regr.rmse"))
```

### Example: Custom PipeOps

```{r, eval = FALSE}
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 = ForecastRecursiveLearner$new(lrn("regr.ranger"))
glrn = as_learner(graph %>>% flrn)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(msr("regr.rmse"))

newdata = generate_newdata(task, 12L)
glrn$predict_newdata(newdata, task)
```

### Example: common target transformations

Some common target transformations in forecasting are:

- differencing (WIP)
- log transformation, see example below
- power transformations such as [Box-Cox](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_boxcox.html) and [Yeo-Johnson](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_yeojohnson.html)
  currently only supported as feature transformation and not target
- scaling/normalization, available see [here](https://mlr3pipelines.mlr-org.com/reference/mlr_pipeops_targettrafoscalerange.html)

```{r, eval = FALSE}
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 = ForecastRecursiveLearner$new(lrn("regr.ranger"))
glrn = as_learner(graph %>>% flrn)
pipeline = ppl("targettrafo", graph = glrn, trafo_pipeop = trafo)
glrn = as_learner(pipeline)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(msr("regr.rmse"))
```

```{r, eval = FALSE}
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 = ForecastRecursiveLearner$new(lrn("regr.ranger"))
glrn = as_learner(graph %>>% flrn)
trafo = po("fcst.targetdiff", lags = 12L)
pipeline = ppl("targettrafo", graph = glrn, trafo_pipeop = trafo)
glrn = as_learner(pipeline)$train(task)
prediction = glrn$predict(task, 142:144)
prediction$score(msr("regr.rmse"))
```