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
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 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: {[1×1 classreg.learning.regr.CompactRegressionTree]}
MaxLag: 7
NumObservations: 465
Properties, Methods
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: {[1×1 classreg.learning.regr.CompactRegressionTree]}
MaxLag: 7
Properties, Methods
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 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.
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
