preparedPredictors
Obtain prepared data used for training or testing in direct forecasting
Since R2023b
Syntax
Description
Examples
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: {7×1 cell}
MaxLag: 7
NumObservations: 372
Properties, Methods
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: {1×19 cell}
ResponseName: 'TemperatureF_Step1'
CategoricalPredictors: [10 11]
ResponseTransform: 'none'
NumTrained: 100
Properties, Methods
Mdl.Learners{7}ans =
CompactRegressionEnsemble
PredictorNames: {1×19 cell}
ResponseName: 'TemperatureF_Step7'
CategoricalPredictors: [10 11]
ResponseTransform: 'none'
NumTrained: 100
Properties, Methods
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'} {0×0 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'} {0×0 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.
Input Arguments
Output Arguments
Limitations
When you use the
preparedPredictorsobject function, the data set must contain at leastMdl.MaxLag + max(Mdl.Horizon)observations. The software requires these observations for creating lagged and leading predictors.
Version History
Introduced in R2023b
