# directforecaster

## Description

`DirectForecaster`

is a multistep forecasting model that uses a
direct strategy in which a separate regression model is trained for each step of the
forecasting horizon. For more information, see Direct Forecasting. Use the `directforecaster`

function to train a
`DirectForecaster`

model with regularly sampled time series
data.

You can use lagged and leading predictors to train the direct forecasting model.
`directforecaster`

creates the appropriate predictors when you specify
the following:

Leading exogenous predictors (

`LeadingPredictors`

)Lag values of the leading exogenous predictors (

`LeadingPredictorLags`

)Lag values of the nonleading exogenous predictors (

`PredictorLags`

)Lag values of the response (

`ResponseLags`

)

For more information, see Forecasting Data.

After creating a `DirectForecaster`

object, you can see how the model
performs on observed test data by using the `loss`

and `predict`

object
functions. You can then use the model to forecast at time steps beyond the available data by
using the `forecast`

object
function.

## Creation

### Syntax

### Description

creates a direct forecasting model `Mdl`

= directforecaster(`Tbl`

,`ResponseVarName`

)`Mdl`

using the regularly sampled
data in `Tbl`

and the response in variable
`ResponseVarName`

in `Tbl`

. The function treats
all variables in `Tbl`

other than `ResponseVarName`

as exogenous predictor variables.

By default, the resulting `Mdl`

object contains one regression
model, with a time horizon of one step ahead. `directforecaster`

uses a
lag value of `1`

to create predictors from the exogenous predictors and
the response variable.

specifies options using one or more name-value arguments in addition to any of the input
argument combinations in previous syntaxes. For example, you can create a model that
forecasts at the first, third, and fifth horizon steps by specifying `Mdl`

= directforecaster(__,`Name=Value`

)```
Horizon=[1
3 5]
```

.

### Input Arguments

`Tbl`

— Training set data

table | timetable

Training set data, specified as a table or timetable. Each row of
`Tbl`

corresponds to one observation, and each column corresponds
to one variable. `Tbl`

must contain the response variable
`ResponseVarName`

.

The software assumes that the observations in

`Tbl`

are regularly sampled. Ensure that no time steps are missing or duplicated and that the observations are in ascending order.By default, the software treats all variables in

`Tbl`

other than`ResponseVarName`

as exogenous predictors. To use a subset of the variables in`Tbl`

as exogenous predictors during model training, specify the`PredictorNames`

name-value argument.

`ResponseVarName`

— Response variable name

name of variable in `Tbl`

Response variable name, specified as the name of a variable in
`Tbl`

. The response variable must contain numeric values.

You must specify `ResponseVarName`

as a character vector or
string scalar. For example, if `Tbl`

stores the response variable
`Response`

as `Tbl.Response`

, then specify it as
`"Response"`

.

**Data Types: **`char`

| `string`

`X`

— Training set exogenous predictor data

numeric matrix | table | timetable

Training set exogenous predictor data, specified as a numeric matrix, table, or
timetable. Each row of `X`

corresponds to one observation, and each
column corresponds to one predictor.

The software assumes that the observations in

`X`

are regularly sampled. Ensure that no time steps are missing or duplicated and that the observations are in ascending order.`X`

and`Y`

must have the same number of observations.If

`X`

is a matrix, you can specify the names of the predictors in the order of their appearance in`X`

by using the`PredictorNames`

name-value argument.If

`X`

is a table or timetable, you can use a subset of the variables in`Tbl`

as exogenous predictors during model training by specifying the`PredictorNames`

name-value argument.

`Y`

— Training set response data

numeric vector | one-column table | one-column timetable

Training set response data, specified as a numeric vector, one-column table, or
one-column timetable. Each row of `Y`

corresponds to one observation.

If

`X`

is a numeric matrix, then`Y`

must be a numeric vector.If

`X`

is a table, then`Y`

must be a numeric vector or one-column table.If

`X`

is a timetable or it is not specified, then`Y`

must be a numeric vector, one-column table, or one-column timetable.

If you specify both `X`

and `Y`

,
then they must have the same number of observations.

**Name-Value Arguments**

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

**Example: **`directforecaster(Tbl,"Y",Horizon=1:3,LeadingPredictors="all",LeadingPredictorLags=0:1,ResponseLags=1:2)`

specifies to forecast at the first, second, and third horizon steps using lagged and
leading predictors. The software treats all exogenous predictors as leading predictors,
and creates one new lagged feature from each exogenous predictor in `Tbl`

and two new lagged features from the response variable `Y`

in
`Tbl`

. The leading predictor lag value of `0`

specifies to also use the unshifted exogenous predictors.

`Horizon`

— Future time steps at which to forecast

`1`

(default) | positive integer vector

Future time steps at which to forecast, specified as a positive integer vector.
The software uses each specified value in `Horizon`

as a
individual horizon step, and trains a regression model that forecasts at that
horizon step.

By default, the software trains one regression model that forecasts one step ahead.

**Example: **`Horizon=1:5`

**Example: **`Horizon=[2 4 6]`

**Data Types: **`single`

| `double`

`Learner`

— Type of regression model to train at each horizon step

`"bag"`

(default) | `"gam"`

| `"gp"`

| `"kernel"`

| `"linear"`

| `"lsboost"`

| `"svm"`

| `"tree"`

| template object

Type of regression model to train at each horizon step, specified as one of the values in this table.

Value | Regression Model Type |
---|---|

`"bag"` or `templateEnsemble` template (with
the method specified as `"Bag"` and the weak learners
specified as `"Tree"` ) | Bagged ensemble of trees |

`"gam"` or `templateGAM` template | General additive model (GAM) |

`"gp"` or `templateGP` template | Gaussian process regression (GPR) |

`"kernel"` or `templateKernel` template | Kernel model |

`"linear"` or `templateLinear` template | Linear model |

`"lsboost"` or `templateEnsemble` template (with
the method specified as `"LSBoost"` and the weak learners
specified as `"Tree"` ) | Boosted ensemble of trees |

`"svm"` or `templateSVM` template | Support vector machine (SVM) |

`"tree"` or `templateTree` template | Decision tree |

**Example: **`Learner="svm"`

**Example: **`Learner=templateEnsemble("LSBoost",50,"Tree")`

`LeadingPredictors`

— List of exogenous predictors whose future values are known

`[]`

(default) | positive integer vector | logical vector | string array | cell array of character vectors | `"all"`

List of exogenous predictors whose future values are known, specified as one of the values in this table.

Value | Description |
---|---|

Positive integer vector | Each entry in the vector is an index value indicating that the
corresponding exogenous predictor is leading. The index values are
between 1 and |

Logical vector | A |

String array or cell array of character vectors | Each element in the array is the name of a leading exogenous
predictor variable. The names must match the entries in
`PredictorNames` . |

`"all"` | All exogenous predictors are leading. |

**Note**

This name-value argument is valid only when you use exogenous predictors.

**Example: **`LeadingPredictors="all"`

**Data Types: **`single`

| `double`

| `logical`

| `string`

| `cell`

`LeadingPredictorLags`

— Predictor lags for preparing leading exogenous predictors

`0`

(default) | nonnegative integer vector | cell array of nonnegative integer vectors

Predictor lags for preparing leading exogenous predictors, specified as a
nonnegative integer vector or a 1-by-*l* cell array of nonnegative
integer vectors, where *l* is the number of leading exogenous predictors.

If

`LeadingPredictorLags`

is a vector, then the software applies each specified lag value in`LeadingPredictorLags`

to all the leading exogenous predictors. That is, for each element`i`

in the vector, the software shifts the leading exogenous predictors backward in time by`i`

steps, relative to the horizon time step. The software uses the resulting features as predictors.If

`LeadingPredictorLags`

is a cell array, then the numeric values in element`i`

of the cell array indicate the lags for leading exogenous predictor`i`

.

**Note**

This name-value argument is valid only when you use leading exogenous
predictors by specifying the `LeadingPredictors`

name-value argument.

**Example: **`LeadingPredictorLags=[0 2 4]`

**Example: **`LeadingPredictorLags={0:1,0:2}`

**Data Types: **`single`

| `double`

| `cell`

`PredictorLags`

— Predictor lags used for preparing nonleading exogenous predictors

`1`

(default) | positive integer vector | cell array of positive integer vectors

Predictor lags used for preparing nonleading exogenous predictors, specified as
a positive integer vector or a 1-by-*q* cell array of positive
integer vectors, where *q* is the number of nonleading exogenous predictors.

If

`PredictorLags`

is a vector, then the software applies each specified lag value in`PredictorLags`

to all the nonleading exogenous predictors. That is, for each element`i`

in the vector, the software shifts the nonleading exogenous predictors backward in time by`i`

steps and uses the resulting feature as a predictor.If

`PredictorLags`

is a cell array, then the numeric values in element`i`

of the cell array indicate the lags for nonleading exogenous predictor`i`

.

**Note**

This name-value argument is valid only when you use nonleading exogenous predictors.

**Example: **`PredictorLags=1:14`

**Example: **`PredictorLags={1:2,1:3,1:2}`

**Data Types: **`single`

| `double`

| `cell`

`ResponseLags`

— Response lags used for preparing predictors

`1`

(default) | positive integer vector | `[]`

Response lags used for preparing predictors, specified as a positive integer
vector. The software applies each specified lag value in
`ResponseLags`

to the response. That is, for each element
`i`

in the vector, the software shifts the response backward in
time by `i`

steps and uses the resulting feature as a predictor. To
create no lagged response variables, specify `ResponseLags`

as
`[]`

.

**Example: **`ResponseLags=1:7`

**Data Types: **`single`

| `double`

`CategoricalPredictors`

— List of categorical exogenous predictors

positive integer vector | logical vector | string array | cell array of character vectors | `"all"`

List of categorical exogenous predictors, specified as one of the values in this table.

Value | Description |
---|---|

Positive integer vector | Each entry in the vector is an index value indicating that the
corresponding exogenous predictor is categorical. The index values are
between 1 and |

Logical vector | A |

String array or cell array of character vectors | Each element in the array is the name of a categorical exogenous
predictor variable. The names must match the entries in
`PredictorNames` . |

`"all"` | All exogenous predictors are categorical. |

By default, if the exogenous predictors are in a numeric matrix, the software
assumes all the exogenous predictors are continuous. If the exogenous predictors are
in a table or timetable, the software assumes they are categorical if they are
logical vectors, `categorical`

vectors, character arrays, string
arrays, or cell arrays of character vectors. However, learners that use decision
trees assume that mathematically ordered `categorical`

vectors are
continuous variables. To identify any other predictors as categorical predictors,
specify them by using the `CategoricalPredictors`

name-value
argument.

The software creates dummy variables based on the `Learner`

name-value argument and the underlying fitting function used to create the
regression models in `Learners`

. For more information on how fitting functions treat
categorical predictors, see Automatic Creation of Dummy Variables.

**Note**

This name-value argument is valid only when you use exogenous predictors.

**Example: **`CategoricalPredictors="all"`

**Data Types: **`single`

| `double`

| `logical`

| `string`

| `cell`

`PredictorNames`

— Names of exogenous predictor variables

string array | cell array of character vectors

Names of the exogenous predictor variables, specified as a string array or cell array of character vectors.

If you supply exogenous predictor data using a numeric matrix, then you can use

`PredictorNames`

to assign names to the exogenous predictor variables.The order of the names in

`PredictorNames`

must correspond to the order of the columns in the matrix.By default,

`PredictorNames`

is`{'x1','x2',...}`

.

If you supply exogenous predictor data using a table or timetable, then you can use

`PredictorNames`

to specify which exogenous variables to use as predictors during training.`PredictorNames`

must be a subset of the variable names in the table or timetable and cannot include the name of the response variable.By default,

`PredictorNames`

contains the names of all variables other than the response variable.

**Note**

This name-value argument is valid only when you use exogenous predictors.

**Example: **`PredictorNames=["Day","Month","Year"]`

**Data Types: **`string`

| `cell`

`ResponseName`

— Name of response variable

`"Y"`

(default) | character vector | string scalar

Name of the response variable `Y`

, specified as a character
vector or a string scalar. `ResponseName`

cannot be the name of a
variable in `X`

.

**Note**

This name-value argument is valid only when you supply `Y`

as a numeric vector.

**Example: **`ResponseName="Temperature"`

**Data Types: **`char`

| `string`

`Partition`

— Time series data partition for cross-validating model

`[]`

(default) | `tspartition`

object

Time series data partition for cross-validating the model, specified as a
`tspartition`

object. The `tspartition`

object can use one of the following
validation schemes: expanding window cross-validation, sliding window
cross-validation, or holdout validation.

If you specify the `Partition`

name-value argument, then
`directforecaster`

returns a `PartitionedDirectForecaster`

object. Otherwise, the function returns a
`DirectForecaster`

object.

**Example: **`Partition=tspartition(size(X,1),"ExpandingWindow",5)`

`UseParallel`

— Flag to run computations in parallel

`false`

(default) | `true`

Flag to run computations in parallel, specified as `true`

or
`false`

. If you specify `UseParallel`

as
`true`

, then the function executes `for`

-loop
iterations by using `parfor`

(Parallel Computing Toolbox). The loop runs in parallel
when you have Parallel Computing Toolbox™.

**Example: **`UseParallel=true`

**Data Types: **`logical`

`NumBins`

— Number of bins for numeric predictors

`[]`

(default) | positive integer scalar

Number of bins for the numeric predictors, specified as a positive integer scalar.

If the

`NumBins`

value is empty (default), then`directforecaster`

does not bin any predictors.If you specify the

`NumBins`

value as a positive integer scalar (`numBins`

), then`directforecaster`

bins every numeric predictor into at most`numBins`

equiprobable bins, and then grows trees on the bin indices instead of the original data.The number of bins can be less than

`numBins`

if a predictor has fewer than`numBins`

unique values.`directforecaster`

does not bin categorical predictors.

When you use a large training data set, this binning option speeds up training
but might cause a decrease in accuracy. You can try setting the
`NumBins`

value to `50`

first, and then change
the value depending on the accuracy and training speed.

**Note**

`directforecaster`

supports the
`NumBins`

name-value argument for trees and ensembles of
trees only. That is, the `Learner`

value must be
`"tree"`

, `"bag"`

, `"gam"`

,
`"lsboost"`

, or a template object created by
`templateTree`

, `templateGAM`

, or
`templateEnsemble`

.

**Example: **`NumBins=50`

**Data Types: **`single`

| `double`

### Output Arguments

`Mdl`

— Trained direct forecasting model

`DirectForecaster`

model object | `PartitionedDirectForecaster`

model object

Trained direct forecasting model, returned as a `DirectForecaster`

or `PartitionedDirectForecaster`

model object.

If you specify the `Partition`

name-value argument, then `directforecaster`

returns a
`PartitionedDirectForecaster`

model object. Otherwise, the function
returns a `DirectForecaster`

model object.

## Properties

### Data Properties

`CategoricalPredictors`

— Indices of categorical exogenous predictors

positive integer vector | `[]`

This property is read-only.

Indices of categorical exogenous predictors, specified as a positive integer vector.
Each index value in `CategoricalPredictors`

indicates that the
corresponding exogenous predictor listed in `PredictorNames`

is
categorical. If none of the exogenous predictors are categorical, then this property is
empty (`[]`

).

**Data Types: **`double`

`NumObservations`

— Number of observations

positive integer scalar

This property is read-only.

Number of observations in the data stored in `X`

and
`Y`

, specified as a positive integer scalar.

**Data Types: **`double`

`PredictorNames`

— Names of exogenous predictors

cell array of character vectors

This property is read-only.

Names of the exogenous predictors, specified as a cell array of character vectors. The
order of the elements in `PredictorNames`

corresponds to the order of
the exogenous predictors in the data argument used to train the model.

**Data Types: **`cell`

`ResponseName`

— Name of response variable

character vector

This property is read-only.

Name of the response variable, specified as a character vector.

**Data Types: **`char`

`X`

— Exogenous predictor data

numeric matrix | table | timetable

This property is read-only.

Exogenous predictor data used to train the model, specified as a numeric matrix,
table, or timetable. Each row of `X`

corresponds to one
observation, and each column corresponds to one variable.

`Y`

— Observed response data

numeric vector | one-column table | one-column timetable

This property is read-only.

Observed response data used to train the model, specified as a numeric vector,
one-column table, or one-column timetable. Each row of `Y`

corresponds to one observation.

### Forecasting Properties

`Horizon`

— Future time steps at which to forecast

positive integer vector

This property is read-only.

Future time steps at which to forecast, specified as a positive integer vector.
`Learners`

contains a trained regression model for each horizon
step. For example, if the `Horizon`

value of a direct forecasting
model `Mdl`

is `[1 3]`

, then
`Mdl.Learners`

contains two regression models: one that forecasts
at horizon step `1`

, and one that forecasts at horizon step
`3`

.

**Data Types: **`double`

`LeadingPredictorLags`

— Predictor lags used for preparing leading exogenous predictors

nonnegative integer vector | cell array of nonnegative integer vectors | `[]`

This property is read-only.

Leading predictor lags used for preparing leading exogenous predictors, specified as a nonnegative integer vector or cell array of nonnegative integer vectors.

If

`LeadingPredictorLags`

is a vector, then for each element`i`

in the vector, the software shifts the leading exogenous predictors backward in time by`i`

steps, relative to the horizon time step. The software uses the resulting features as predictors. When the`LeadingPredictorLags`

value is`0`

, the software uses the unshifted leading predictors.For example, if the

`Horizon`

value of a direct forecasting model is`3`

and the`LeadingPredictorLags`

value is`0`

, then the software uses the unshifted leading predictor values at horizon step`3`

as predictor values.If

`LeadingPredictorLags`

is a cell array, then the numeric values in element`i`

of the cell array indicate the lags for leading exogenous predictor`i`

.

If no leading predictor lags are used, then this property is empty (`[]`

).

**Data Types: **`double`

| `cell`

`LeadingPredictors`

— Indices of leading exogenous predictors

positive integer vector | `[]`

This property is read-only.

Indices of the leading exogenous predictors, specified as a positive integer vector. Leading predictors are predictors for which future values are known. Each index value in `LeadingPredictors`

indicates that the corresponding exogenous predictor listed in `PredictorNames`

is leading. If no exogenous predictors are leading predictors, then this property is empty (`[]`

).

**Data Types: **`double`

`Learners`

— Compact regression models trained at different horizon steps

cell array of regression model objects

This property is read-only.

Compact regression models trained at different horizon steps, specified as a cell array of regression model objects. That is, for a direct forecasting model `Mdl`

, the software trains the regression model `Mdl.Learners{1}`

at horizon step `Mdl.Horizon(1)`

.

This table lists the possible compact regression models.

Regression Model Type | Model Object |
---|---|

Bagged or boosted ensemble of trees | `CompactRegressionEnsemble` |

General additive model (GAM) | `CompactRegressionGAM` |

Gaussian process regression (GPR) | `CompactRegressionGP` |

Kernel model | `RegressionKernel` |

Linear model | `RegressionLinear` |

Support vector machine (SVM) | `CompactRegressionSVM` |

Decision tree | `CompactRegressionTree` |

**Data Types: **`cell`

`LearnerTemplate`

— Template for regression models

output of template function

This property is read-only.

Template for the regression models in `Learners`

, specified as
the output of one of these template functions.

Template Function | Description |
---|---|

`templateEnsemble` | Ensemble learning template, with the ensemble aggregation method specified as
`"Bag"` or `"LSBoost"` |

`templateGAM` | General additive model template |

`templateGP` | Gaussian process regression model template |

`templateKernel` | Kernel model template |

`templateLinear` | Linear learner template |

`templateSVM` | Support vector machine template |

`templateTree` | Decision tree template |

`MaxLag`

— Maximum lag value

nonnegative integer scalar

This property is read-only.

Maximum lag value, specified as a nonnegative integer scalar. The `MaxLag`

value depends on the values in `ResponseLags`

,
`PredictorLags`

, and `LeadingPredictorLags`

.
Specifically, the software computes the maximum lag as
follows:

```
MaxLag = max([0,ResponseLags,PredictorLags, ...
LeadingPredictorLags - min(Horizon) + 1])
```

**Data Types: **`double`

`PredictorLags`

— Predictor lags used for preparing nonleading exogenous predictors

positive integer vector | cell array of positive integer vectors | `[]`

This property is read-only.

Predictor lags used for preparing nonleading exogenous predictors, specified as a positive integer vector or cell array of positive integer vectors.

If

`PredictorLags`

is a vector, then for each element`i`

in the vector, the software shifts the nonleading exogenous predictors backward in time by`i`

steps and uses the resulting features as predictors.If

`PredictorLags`

is a cell array, then the numeric values in element`i`

of the cell array indicate the lags for nonleading exogenous predictor`i`

.

If no predictor lags are used, then this property is empty (`[]`

).

**Data Types: **`double`

| `cell`

`ResponseLags`

— Response lags used for preparing predictors

positive integer vector | `[]`

This property is read-only.

Response lags used for preparing predictors, specified as a positive integer vector.
Each element in `ResponseLags`

indicates the number of time steps by
which to shift the response backward in time. The resulting feature is used as a
predictor. If no response lags are used, then this property is empty
(`[]`

).

**Data Types: **`double`

### Prepared Data Properties

`PreparedCategoricalPredictors`

— Indices of prepared categorical predictors

positive integer vector | `[]`

This property is read-only.

Indices of the prepared categorical predictors, specified as a positive integer vector. Each index value in `PreparedCategoricalPredictors`

indicates that the corresponding predictor listed in `PreparedPredictorNames`

is categorical. If no prepared predictors are categorical predictors, then this property is empty (`[]`

).

**Data Types: **`double`

`PreparedPredictorNames`

— Names of prepared predictors

cell array of character vectors

This property is read-only.

Names of the prepared predictors, specified as a cell array of character vectors. These
prepared predictors include variables created from both the exogenous predictor
variables and the response variable used to train the direct forecasting model. Not
every predictor is used at every horizon step. To see which predictors are used at a
specific horizon step, consult the `PreparedPredictorsPerHorizon`

table.

**Data Types: **`cell`

`PreparedPredictorsPerHorizon`

— Prepared predictors at each horizon step

table of logical values

This property is read-only.

Prepared predictors at each horizon step, specified as a table of logical values. Each row of the table corresponds to a horizon step, and each column of the table corresponds to a prepared predictor as listed in `PreparedPredictorNames`

.

For a direct forecasting model `Mdl`

, the logical value in row `i`

and column `j`

indicates whether the software uses prepared predictor `Mdl.PreparedPredictorNames(j)`

at horizon step `Mdl.Horizon(i)`

. If the value is `1`

(`true`

), then the software uses the predictor. If the value is `0`

(`false`

), then the software does not use the predictor.

**Data Types: **`table`

`PreparedResponseNames`

— Names of prepared responses at each horizon step

cell array of character vectors

This property is read-only.

Names of the prepared responses at each horizon step, specified as a cell array of character
vectors. That is, element `i`

of
`PreparedResponseNames`

is the name of the response variable at
the horizon step specified by element `i`

of
`Horizon`

.

For example, given a direct forecasting model `Mdl`

, the name of the response
variable at horizon step `Mdl.Horizon(1)`

,
`Mdl.PreparedResponseNames{1}`

, matches the response variable name
used in the first regression model in `Learners`

(`Mdl.Learners{1}.ResponseName`

).

**Data Types: **`cell`

## Object Functions

`compact` | Reduce size of direct forecasting model |

`crossval` | Cross-validate direct forecasting model |

`loss` | Loss at each horizon step |

`predict` | Predict response at time steps in observed test data |

`forecast` | Forecast response at time steps beyond available data |

`preparedPredictors` | Obtain prepared data used for training or testing in direct forecasting |

## Examples

### Calculate Test Set Mean Squared Error of Direct Forecasting Model

Calculate the test set mean squared error (MSE) of a direct forecasting model.

Load the sample file `TemperatureData.csv`

, which contains average daily temperatures from January 2015 through July 2016. Read the file into a table. Observe the first eight observations in the table.

```
temperatures = readtable("TemperatureData.csv");
head(temperatures)
```

Year Month Day TemperatureF ____ ___________ ___ ____________ 2015 {'January'} 1 23 2015 {'January'} 2 31 2015 {'January'} 3 25 2015 {'January'} 4 39 2015 {'January'} 5 29 2015 {'January'} 6 12 2015 {'January'} 7 10 2015 {'January'} 8 4

For this example, use a subset of the temperature data that omits the first 100 observations.

Tbl = temperatures(101:end,:);

Create a `datetime`

variable `t`

that contains the year, month, and day information for each observation in `Tbl`

. Then, use `t`

to convert `Tbl`

into a timetable.

numericMonth = month(datetime(Tbl.Month, ... InputFormat="MMMM",Locale="en_US")); t = datetime(Tbl.Year,numericMonth,Tbl.Day); Tbl.Time = t; Tbl = table2timetable(Tbl);

Plot the temperature values in `Tbl`

over time.

plot(Tbl.Time,Tbl.TemperatureF) xlabel("Date") ylabel("Temperature in Fahrenheit")

Partition the temperature data into training and test sets by using `tspartition`

. Reserve 20% of the observations for testing.

```
partition = tspartition(size(Tbl,1),"Holdout",0.20);
trainingTbl = Tbl(training(partition),:);
testTbl = Tbl(test(partition),:);
```

Create a full direct forecasting model by using the data in `trainingTbl`

. Train the model using a decision tree learner. All three of the predictors (`Year`

, `Month`

, and `Day`

) are leading predictors because their future values are known. To create new predictors by shifting the leading predictor and response variables backward in time, specify the leading predictor lags and the response variable lags.

Mdl = directforecaster(trainingTbl,"TemperatureF", ... Learner="tree", ... LeadingPredictors="all",LeadingPredictorLags={0:1,0:1,0:7}, ... ResponseLags=1:7)

Mdl = DirectForecaster Horizon: 1 ResponseLags: [1 2 3 4 5 6 7] LeadingPredictors: [1 2 3] LeadingPredictorLags: {[0 1] [0 1] [0 1 2 3 4 5 6 7]} ResponseName: 'TemperatureF' PredictorNames: {'Year' 'Month' 'Day'} CategoricalPredictors: [2] Learners: {[1x1 classreg.learning.regr.CompactRegressionTree]} MaxLag: [7] NumObservations: [372]

`Mdl`

is a `DirectForecaster`

model object. By default, the horizon is one step ahead. That is, `Mdl`

predicts a value that is one step into the future.

Calculate the test set MSE. Smaller MSE values indicate better performance.

testMSE = loss(Mdl,testTbl)

testMSE = 61.0849

### Predict Response for Observed Test Data and Forecast Response Beyond Available Data

After creating a `DirectForecaster`

object, see how the model performs on observed test data by using the `predict`

object function. Then use the model to forecast at time steps beyond the available data by using the `forecast`

object function.

Load the sample file `TemperatureData.csv`

, which contains average daily temperatures from January 2015 through July 2016. Read the file into a table. Observe the first eight observations in the table.

```
temperatures = readtable("TemperatureData.csv");
head(temperatures)
```

Year Month Day TemperatureF ____ ___________ ___ ____________ 2015 {'January'} 1 23 2015 {'January'} 2 31 2015 {'January'} 3 25 2015 {'January'} 4 39 2015 {'January'} 5 29 2015 {'January'} 6 12 2015 {'January'} 7 10 2015 {'January'} 8 4

For this example, use a subset of the temperature data that omits the first 100 observations.

Tbl = temperatures(101:end,:);

Create a `datetime`

variable `t`

that contains the year, month, and day information for each observation in `Tbl`

. Then, use `t`

to convert `Tbl`

into a timetable.

numericMonth = month(datetime(Tbl.Month, ... InputFormat="MMMM",Locale="en_US")); t = datetime(Tbl.Year,numericMonth,Tbl.Day); Tbl.Time = t; Tbl = table2timetable(Tbl);

Plot the temperature values in `Tbl`

over time.

plot(Tbl.Time,Tbl.TemperatureF) xlabel("Date") ylabel("Temperature in Fahrenheit")

Partition the temperature data into training and test sets by using `tspartition`

. Reserve 20% of the observations for testing.

```
partition = tspartition(size(Tbl,1),"Holdout",0.20);
trainingTbl = Tbl(training(partition),:);
testTbl = Tbl(test(partition),:);
```

Create a full direct forecasting model by using the data in `trainingTbl`

. Train the model using a decision tree learner. All three of the predictors (`Year`

, `Month`

, and `Day`

) are leading predictors because their future values are known. To create new predictors by shifting the leading predictor and response variables backward in time, specify the leading predictor lags and the response variable lags.

Mdl = directforecaster(trainingTbl,"TemperatureF", ... Learner="tree", ... LeadingPredictors="all",LeadingPredictorLags={0:1,0:1,0:7}, ... ResponseLags=1:7)

Mdl = DirectForecaster Horizon: 1 ResponseLags: [1 2 3 4 5 6 7] LeadingPredictors: [1 2 3] LeadingPredictorLags: {[0 1] [0 1] [0 1 2 3 4 5 6 7]} ResponseName: 'TemperatureF' PredictorNames: {'Year' 'Month' 'Day'} CategoricalPredictors: [2] Learners: {[1x1 classreg.learning.regr.CompactRegressionTree]} MaxLag: [7] NumObservations: [372]

`Mdl`

is a `DirectForecaster`

model object. By default, the horizon is one step ahead. That is, `Mdl`

predicts a value that is one step into the future.

For each test set observation, predict the temperature value using `Mdl`

.

predictedY = predict(Mdl,testTbl)

`predictedY=`*93×1 timetable*
Time TemperatureF_Step1
___________ __________________
16-Apr-2016 49.398
17-Apr-2016 39.419
18-Apr-2016 39.419
19-Apr-2016 45.333
20-Apr-2016 35.867
21-Apr-2016 34.222
22-Apr-2016 45.333
23-Apr-2016 66.392
24-Apr-2016 44.111
25-Apr-2016 49
26-Apr-2016 49
27-Apr-2016 34.222
28-Apr-2016 43.333
29-Apr-2016 34.222
30-Apr-2016 34.222
01-May-2016 34.222
⋮

Plot the true response values and the predicted response values for the test set observations.

plot(testTbl.Time,testTbl.TemperatureF) hold on plot(predictedY.Time,predictedY.TemperatureF_Step1,"--") hold off legend("True","Predicted",Location="southeast") xlabel("Date") ylabel("Temperature in Fahrenheit")

Overall, the direct forecasting model is able to predict the trend in temperatures.

Retrain the direct forecasting model using the training and test data. To forecast temperatures one week beyond the available data, specify the horizon steps as one to seven steps ahead.

finalMdl = directforecaster(Tbl,"TemperatureF", ... Learner="tree", ... LeadingPredictors="all",LeadingPredictorLags={0:1,0:1,0:7}, ... ResponseLags=1:7,Horizon=1:7)

finalMdl = DirectForecaster Horizon: [1 2 3 4 5 6 7] ResponseLags: [1 2 3 4 5 6 7] LeadingPredictors: [1 2 3] LeadingPredictorLags: {[0 1] [0 1] [0 1 2 3 4 5 6 7]} ResponseName: 'TemperatureF' PredictorNames: {'Year' 'Month' 'Day'} CategoricalPredictors: [2] Learners: {7x1 cell} MaxLag: [7] NumObservations: [465]

`finalMdl`

is a `DirectForecaster`

model object that consists of seven regression models: `finalMdl.Learners{1}`

, which predicts one step into the future; `finalMdl.Learners{2}`

, which predicts two steps into the future; and so on.

Because `finalMdl`

uses the unshifted values of the leading predictors `Year`

, `Month`

, and `Day`

as predictor values, you must specify these values for the specified horizon steps in the call to `forecast`

. For the week after the last available observation in `Tbl`

, create a timetable `forecastData`

with the year, month, and day values.

forecastTime = Tbl.Time(end,:)+1:Tbl.Time(end,:)+7; forecastYear = year(forecastTime); forecastMonth = month(forecastTime,"name"); forecastDay = day(forecastTime); forecastData = timetable(forecastTime',forecastYear', ... forecastMonth',forecastDay',VariableNames=["Year","Month","Day"])

`forecastData=`*7×3 timetable*
Time Year Month Day
___________ ____ ________ ___
18-Jul-2016 2016 {'July'} 18
19-Jul-2016 2016 {'July'} 19
20-Jul-2016 2016 {'July'} 20
21-Jul-2016 2016 {'July'} 21
22-Jul-2016 2016 {'July'} 22
23-Jul-2016 2016 {'July'} 23
24-Jul-2016 2016 {'July'} 24

Forecast the temperature at each horizon step using `finalMdl`

.

forecastY = forecast(finalMdl,Tbl,LeadingData=forecastData)

`forecastY=`*7×1 timetable*
Time TemperatureF
___________ ____________
18-Jul-2016 62.375
19-Jul-2016 64.5
20-Jul-2016 66.889
21-Jul-2016 66.889
22-Jul-2016 70.5
23-Jul-2016 74.25
24-Jul-2016 74.25

Plot the observed temperatures for the test set data and the forecast temperatures.

plot(testTbl.Time,testTbl.TemperatureF) hold on plot([testTbl.Time(end);forecastY.Time], ... [testTbl.TemperatureF(end);forecastY.TemperatureF],"--") hold off legend("Observed Data","Forecast Data", ... Location="southeast") xlabel("Date") ylabel("Temperature in Fahrenheit")

### Prepared Predictor Data for Forecasting

When you perform direct forecasting using `directforecaster`

, the function creates lagged and leading predictors from the training data before fitting a `DirectForecaster`

model. Similarly, the `loss`

and `predict`

object functions reformat the test data before computing loss and prediction values, respectively.

This example shows how to access the prepared predictor data used by direct forecasting models for training and testing.

Load the sample file `TemperatureData.csv`

, which contains average daily temperatures from January 2015 through July 2016. Read the file into a table. Observe the first eight observations in the table.

```
temperatures = readtable("TemperatureData.csv");
head(temperatures)
```

Year Month Day TemperatureF ____ ___________ ___ ____________ 2015 {'January'} 1 23 2015 {'January'} 2 31 2015 {'January'} 3 25 2015 {'January'} 4 39 2015 {'January'} 5 29 2015 {'January'} 6 12 2015 {'January'} 7 10 2015 {'January'} 8 4

For this example, use a subset of the temperature data that omits the first 100 observations.

Tbl = temperatures(101:end,:);

Create a `datetime`

variable `t`

that contains the year, month, and day information for each observation in `Tbl`

. Then, use `t`

to convert `Tbl`

into a timetable.

numericMonth = month(datetime(Tbl.Month, ... InputFormat="MMMM",Locale="en_US")); t = datetime(Tbl.Year,numericMonth,Tbl.Day); Tbl.Time = t; Tbl = table2timetable(Tbl);

Plot the temperature values in `Tbl`

over time.

plot(Tbl.Time,Tbl.TemperatureF) xlabel("Date") ylabel("Temperature in Fahrenheit")

Partition the temperature data into training and test sets by using `tspartition`

. Reserve 20% of the observations for testing.

```
partition = tspartition(size(Tbl,1),"Holdout",0.20);
trainingTbl = Tbl(training(partition),:);
testTbl = Tbl(test(partition),:);
```

Create a full direct forecasting model by using the data in `trainingTbl`

. Specify the horizon steps as one to seven steps ahead. Train a model at each horizon step using a boosted ensemble of trees. All three of the predictors (`Year`

, `Month`

, and `Day`

) are leading predictors because their future values are known.

To create new predictors by shifting the leading predictor and response variables backward in time, specify the leading predictor lags and the response variable lags. For this example, use the following as predictors values: the current and previous `Year`

values, the current and previous `Month`

values, the current and previous seven `Day`

values, and the previous seven `TemperatureF`

values.

Mdl = directforecaster(trainingTbl,"TemperatureF", ... Horizon=1:7,LeadingPredictors="all", ... LeadingPredictorLags={0:1,0:1,0:7}, ... ResponseLags=1:7)

Mdl = DirectForecaster Horizon: [1 2 3 4 5 6 7] ResponseLags: [1 2 3 4 5 6 7] LeadingPredictors: [1 2 3] LeadingPredictorLags: {[0 1] [0 1] [0 1 2 3 4 5 6 7]} ResponseName: 'TemperatureF' PredictorNames: {'Year' 'Month' 'Day'} CategoricalPredictors: [2] Learners: {7x1 cell} MaxLag: [7] NumObservations: [372]

`Mdl`

is a `DirectForecaster`

model object. `Mdl`

consists of seven regression models: `Mdl.Learners{1}`

, which predicts one step into the future; `Mdl.Learners{2}`

, which predicts two steps into the future; and so on.

Compare the first and seventh regression models in `Mdl`

.

Mdl.Learners{1}

ans = CompactRegressionEnsemble PredictorNames: {1x19 cell} ResponseName: 'TemperatureF_Step1' CategoricalPredictors: [10 11] ResponseTransform: 'none' NumTrained: 100

Mdl.Learners{7}

ans = CompactRegressionEnsemble PredictorNames: {1x19 cell} ResponseName: 'TemperatureF_Step7' CategoricalPredictors: [10 11] ResponseTransform: 'none' NumTrained: 100

The regression models in `Mdl`

are all `CompactRegressionEnsemble`

objects. Because the models are compact, they do not include the predictor data used to train them.

To see the data used to train the regression models in `Mdl`

, use the `preparedPredictors`

object function.

Observe the prepared predictor data used to train `Mdl.Learners{1}`

. By default, `preparedPredictors`

returns the prepared predictor data used at horizon step `Mdl.Horizon(1)`

, which in this case is one step ahead.

prepTrainingTbl1 = preparedPredictors(Mdl,trainingTbl)

`prepTrainingTbl1=`*372×19 timetable*
Time TemperatureF_Lag1 TemperatureF_Lag2 TemperatureF_Lag3 TemperatureF_Lag4 TemperatureF_Lag5 TemperatureF_Lag6 TemperatureF_Lag7 Year_Step1 Year_Lag1 Month_Step1 Month_Lag1 Day_Step1 Day_Lag1 Day_Lag2 Day_Lag3 Day_Lag4 Day_Lag5 Day_Lag6 Day_Lag7
___________ _________________ _________________ _________________ _________________ _________________ _________________ _________________ __________ _________ ___________ __________ _________ ________ ________ ________ ________ ________ ________ ________
10-Apr-2015 NaN NaN NaN NaN NaN NaN NaN 2015 NaN {'April'} {0x0 char} 10 NaN NaN NaN NaN NaN NaN NaN
11-Apr-2015 41 NaN NaN NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 11 10 NaN NaN NaN NaN NaN NaN
12-Apr-2015 45 41 NaN NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 12 11 10 NaN NaN NaN NaN NaN
13-Apr-2015 49 45 41 NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 13 12 11 10 NaN NaN NaN NaN
14-Apr-2015 50 49 45 41 NaN NaN NaN 2015 2015 {'April'} {'April' } 14 13 12 11 10 NaN NaN NaN
15-Apr-2015 54 50 49 45 41 NaN NaN 2015 2015 {'April'} {'April' } 15 14 13 12 11 10 NaN NaN
16-Apr-2015 54 54 50 49 45 41 NaN 2015 2015 {'April'} {'April' } 16 15 14 13 12 11 10 NaN
17-Apr-2015 46 54 54 50 49 45 41 2015 2015 {'April'} {'April' } 17 16 15 14 13 12 11 10
18-Apr-2015 51 46 54 54 50 49 45 2015 2015 {'April'} {'April' } 18 17 16 15 14 13 12 11
19-Apr-2015 47 51 46 54 54 50 49 2015 2015 {'April'} {'April' } 19 18 17 16 15 14 13 12
20-Apr-2015 41 47 51 46 54 54 50 2015 2015 {'April'} {'April' } 20 19 18 17 16 15 14 13
21-Apr-2015 41 41 47 51 46 54 54 2015 2015 {'April'} {'April' } 21 20 19 18 17 16 15 14
22-Apr-2015 51 41 41 47 51 46 54 2015 2015 {'April'} {'April' } 22 21 20 19 18 17 16 15
23-Apr-2015 50 51 41 41 47 51 46 2015 2015 {'April'} {'April' } 23 22 21 20 19 18 17 16
24-Apr-2015 40 50 51 41 41 47 51 2015 2015 {'April'} {'April' } 24 23 22 21 20 19 18 17
25-Apr-2015 39 40 50 51 41 41 47 2015 2015 {'April'} {'April' } 25 24 23 22 21 20 19 18
⋮

`prepTrainingTbl1`

contains lagged predictors (with `Lag`

in their names) and leading predictors (with `Step`

in their names). The table contains missing values due to the creation of these prepared predictors. For example, `TemperatureF_Lag1`

contains a missing value at time `10-Apr-2015`

because the temperature at time `09-Apr-2015`

is not known.

Observe the prepared predictor data used to train `Mdl.Learners{7}`

.

```
prepTrainingTbl7 = preparedPredictors(Mdl,trainingTbl, ...
HorizonStep=7)
```

`prepTrainingTbl7=`*372×19 timetable*
Time TemperatureF_Lag1 TemperatureF_Lag2 TemperatureF_Lag3 TemperatureF_Lag4 TemperatureF_Lag5 TemperatureF_Lag6 TemperatureF_Lag7 Year_Step7 Year_Step6 Month_Step7 Month_Step6 Day_Step7 Day_Step6 Day_Step5 Day_Step4 Day_Step3 Day_Step2 Day_Step1 Day_Lag1
___________ _________________ _________________ _________________ _________________ _________________ _________________ _________________ __________ __________ ___________ ___________ _________ _________ _________ _________ _________ _________ _________ ________
10-Apr-2015 NaN NaN NaN NaN NaN NaN NaN 2015 NaN {'April'} {0x0 char} 10 NaN NaN NaN NaN NaN NaN NaN
11-Apr-2015 NaN NaN NaN NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 11 10 NaN NaN NaN NaN NaN NaN
12-Apr-2015 NaN NaN NaN NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 12 11 10 NaN NaN NaN NaN NaN
13-Apr-2015 NaN NaN NaN NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 13 12 11 10 NaN NaN NaN NaN
14-Apr-2015 NaN NaN NaN NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 14 13 12 11 10 NaN NaN NaN
15-Apr-2015 NaN NaN NaN NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 15 14 13 12 11 10 NaN NaN
16-Apr-2015 NaN NaN NaN NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 16 15 14 13 12 11 10 NaN
17-Apr-2015 41 NaN NaN NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 17 16 15 14 13 12 11 10
18-Apr-2015 45 41 NaN NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 18 17 16 15 14 13 12 11
19-Apr-2015 49 45 41 NaN NaN NaN NaN 2015 2015 {'April'} {'April' } 19 18 17 16 15 14 13 12
20-Apr-2015 50 49 45 41 NaN NaN NaN 2015 2015 {'April'} {'April' } 20 19 18 17 16 15 14 13
21-Apr-2015 54 50 49 45 41 NaN NaN 2015 2015 {'April'} {'April' } 21 20 19 18 17 16 15 14
22-Apr-2015 54 54 50 49 45 41 NaN 2015 2015 {'April'} {'April' } 22 21 20 19 18 17 16 15
23-Apr-2015 46 54 54 50 49 45 41 2015 2015 {'April'} {'April' } 23 22 21 20 19 18 17 16
24-Apr-2015 51 46 54 54 50 49 45 2015 2015 {'April'} {'April' } 24 23 22 21 20 19 18 17
25-Apr-2015 47 51 46 54 54 50 49 2015 2015 {'April'} {'April' } 25 24 23 22 21 20 19 18
⋮

Because `Mdl.Learners{7}`

predicts seven steps ahead, `prepTrainingTbl7`

contains different predictors from the predictors in `prepTrainingTbl1`

. For example, `prepTrainingTbl7`

contains the predictors `Year_Step7`

and `Year_Step6`

instead of the predictors `Year_Step1`

and `Year_Lag1`

in `prepTrainingTbl1`

. The step numbers indicate the horizon steps (that is, the number of time steps ahead).

Compute the test set mean squared error at each horizon step.

mse = loss(Mdl,testTbl)

`mse = `*1×7*
32.1256 45.3297 49.8831 49.3660 55.7613 50.4300 53.6758

Obtain the prepared test set predictor data used by `Mdl.Learners{1}`

to compute `mse(1)`

. Compare the variables in `prepTestTbl1`

and `prepTrainingTbl1`

.

```
prepTestTbl1 = preparedPredictors(Mdl,testTbl);
isequal(prepTrainingTbl1.Properties.VariableNames, ...
prepTestTbl1.Properties.VariableNames)
```

`ans = `*logical*
1

The prepared predictors in `prepTestTbl1`

and `prepTrainingTbl1`

are the same.

Similarly, obtain the prepared test set predictor data used by `Mdl.Learners{7}`

to compute `mse(7)`

. Compare the variables in `prepTestTbl7`

and `prepTrainingTbl7`

.

prepTestTbl7 = preparedPredictors(Mdl,testTbl, ... HorizonStep=7); isequal(prepTrainingTbl7.Properties.VariableNames, ... prepTestTbl7.Properties.VariableNames)

`ans = `*logical*
1

The prepared predictors in `prepTestTbl7`

and `prepTrainingTbl7`

are also the same.

## More About

### Direct Forecasting

Direct forecasting is a forecasting technique that uses separate models to predict the response values at different future time steps (horizon steps). This technique differs from recursive forecasting, where one model is used to predict values at multiple horizon steps.

The software prepares the predictor data for each model and then uses the model to forecast at a particular horizon step.

For more information, see `PreparedPredictorsPerHorizon`

and `Horizon`

.

### Forecasting Data

The `directforecaster`

function accepts data sets with regularly sampled values
that include a response variable and exogenous predictors (optional). That is, the time
steps between consecutive observations are the same. In this context, exogenous predictors
are predictors that are not derived from the response variable.

Consider the following data set.

In this example, the row times in `MeasurementTime`

show that the time difference between consecutive observations is one hour. The times `18-Dec-2015 14:00:00`

and `18-Dec-2015 15:00:00`

are future time steps that exist beyond the available data. They represent the first and second horizon steps. (See `Horizon`

.)

Suppose the `Temp`

variable is the response variable. The
`Pressure`

, `WindSpeed`

, and
`WorkHours`

variables are exogenous predictors. The
`WorkHours`

variable is a leading exogenous predictor because its
future values are known. (See `LeadingPredictors`

.)

Before fitting a forecasting model, the software creates time-shifted features from the response and exogenous predictors based on user-specified lag values. In this example, the red rectangles indicate a `ResponseLags`

value of `1`

, `PredictorLags`

value of `[1 2 3]`

, and `LeadingPredictorLags`

value of `[0 1]`

at horizon step `1`

(`18-Dec-2015 14:00:00`

).

## Extended Capabilities

### Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run in parallel, set the `UseParallel`

name-value argument to
`true`

in the call to this function.

For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).

## Version History

**Introduced in R2023b**

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