CompactDirectForecaster
Description
CompactDirectForecaster
is a compact version of a DirectForecaster
model object for direct forecasting. The compact model does not include the time series data
(X
and Y
) used for training the full model.
Therefore, you cannot perform some tasks, such as a cross-validation, using the compact
model.
Creation
Create a CompactDirectForecaster
object from a full DirectForecaster
model object by using compact
.
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
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
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 elementi
in the vector, the software shifts the leading exogenous predictors backward in time byi
steps, relative to the horizon time step. The software uses the resulting features as predictors. When theLeadingPredictorLags
value is0
, the software uses the unshifted leading predictors.For example, if the
Horizon
value of a direct forecasting model is3
and theLeadingPredictorLags
value is0
, then the software uses the unshifted leading predictor values at horizon step3
as predictor values.If
LeadingPredictorLags
is a cell array, then the numeric values in elementi
of the cell array indicate the lags for leading exogenous predictori
.
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
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 elementi
in the vector, the software shifts the nonleading exogenous predictors backward in time byi
steps and uses the resulting features as predictors.If
PredictorLags
is a cell array, then the numeric values in elementi
of the cell array indicate the lags for nonleading exogenous predictori
.
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
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
Reduce Size of Direct Forecasting Model
Reduce the size of a full direct forecasting model by removing the training data from the model. You can use a compact model to improve memory efficiency.
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")
Create a full direct forecasting model by using the data in Tbl
. 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(Tbl,"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: 465
Mdl
is a DirectForecaster
object. By default, the horizon is one step ahead. That is, Mdl
predicts a value that is one step into the future.
Reduce the size of the model by using the compact
object function.
compactMdl = compact(Mdl)
compactMdl = CompactDirectForecaster 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
compactMdl
is a CompactDirectForecaster
model object. compactMdl
contains fewer properties than the full model Mdl
.
Display the amount of memory used by each direct forecasting model.
whos("Mdl","compactMdl")
Name Size Bytes Class Attributes Mdl 1x1 119523 timeseries.forecaster.DirectForecaster compactMdl 1x1 43983 timeseries.forecaster.CompactDirectForecaster
The full model is larger than the compact model.
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
).
Version History
Introduced in R2023b
See Also
DirectForecaster
| compact
| loss
| predict
| forecast
| preparedPredictors
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