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Cross-validation aggregation for forecasting
www.lancs.ac.uk
Devon K. BarrowSven F. Crone
1. Motivation2. Cross-validation and model selection3. Cross-validation aggregation4. Empirical evaluation5. Conclusions and future work
Outline
Cross validation aggregation for forecasting Motivation 1
• Scenario:– The statistician constructs a model and wishes to estimate the error
rate of this model when used to predict future values
Motivation
Cross validation aggregation for forecasting Motivation 2
• Scenario:– The statistician constructs a model and wishes to estimate the error
rate of this model when used to predict future valuesBootstrapping (Efron ,1979) Cross validation (Stone, 1974)
Goal Estimating generalisation error Estimating generalisation error
Motivation
Cross validation aggregation for forecasting Motivation 2
• Scenario:– The statistician constructs a model and wishes to estimate the error
rate of this model when used to predict future valuesBootstrapping (Efron ,1979) Cross validation (Stone, 1974)
Goal Estimating generalisation error Estimating generalisation error
Motivation
Cross validation aggregation for forecasting Motivation 2
Procedure Random sampling with replacement from a single learning set (bootstrap samples). The validation set is the same as the original learning set.
Splits the data into mutually exclusive subsets, using one subset as a set to train each model, and the remaining part as a validation sample (Arlot & Celisse, 2010)
• Scenario:– The statistician constructs a model and wishes to estimate the error
rate of this model when used to predict future valuesBootstrapping (Efron ,1979) Cross validation (Stone, 1974)
Goal Estimating generalisation error Estimating generalisation error
Motivation
Cross validation aggregation for forecasting Motivation 2
Procedure Random sampling with replacement from a single learning set (bootstrap samples). The validation set is the same as the original learning set.
Splits the data into mutually exclusive subsets, using one subset as a set to train each model, and the remaining part as a validation sample (Arlot & Celisse, 2010)
Properties Low variance but is downward biased (Efron and Tibshirani, 1997)
Generalization error estimate is nearly unbiased but can be highly variable (Efron and Tibshirani, 1997)
• Scenario:– The statistician constructs a model and wishes to estimate the error
rate of this model when used to predict future valuesBootstrapping (Efron ,1979) Cross validation (Stone, 1974)
Goal Estimating generalisation error Estimating generalisation error
Motivation
Cross validation aggregation for forecasting Motivation 2
Procedure Random sampling with replacement from a single learning set (bootstrap samples). The validation set is the same as the original learning set.
Splits the data into mutually exclusive subsets, using one subset as a set to train each model, and the remaining part as a validation sample (Arlot & Celisse, 2010)
Properties Low variance but is downward biased (Efron and Tibshirani, 1997)
Generalization error estimate is nearly unbiased but can be highly variable (Efron and Tibshirani, 1997)
1996 - Breiman introduces bootstrapping and aggregation
• Scenario:– The statistician constructs a model and wishes to estimate the error
rate of this model when used to predict future valuesBootstrapping (Efron ,1979) Cross validation (Stone, 1974)
Goal Estimating generalisation error Estimating generalisation error
Motivation
Cross validation aggregation for forecasting Motivation 2
Procedure Random sampling with replacement from a single learning set (bootstrap samples). The validation set is the same as the original learning set.
Splits the data into mutually exclusive subsets, using one subset as a set to train each model, and the remaining part as a validation sample (Arlot & Celisse, 2010)
Properties Low variance but is downward biased (Efron and Tibshirani, 1997)
Generalization error estimate is nearly unbiased but can be highly variable (Efron and Tibshirani, 1997)
Forecast aggregation
Bagging (Breiman 1996) – aggregates the outputs of models trained on bootstrap samples
•–
(a) Published items in each year (b) Citations in Each Year
Bootstrapping (Efron ,1979) Cross validation (Stone, 1974)
Goal Estimating generalisation error Estimating generalisation error
Motivation
Cross validation aggregation for forecasting Motivation 2
Procedure Random sampling with replacement from a single learning set (bootstrap samples). The validation set is the same as the original learning set.
Splits the data into mutually exclusive subsets, using one subset as a set to train each model, and the remaining part as a validation sample (Arlot & Celisse, 2010)
Properties Low variance but is downward biased (Efron and Tibshirani, 1997)
Generalization error estimate is nearly unbiased but can be highly variable (Efron and Tibshirani, 1997)
Forecast aggregation
Bagging (Breiman 1996) – aggregates the outputs of models trained on bootstrap samples
Bagging for time series forecasting:• Forecasting with many
predictors (Watson 2005)
• Macro-economic time series e.g. consumer price inflation (Inoue & Kilian 2008)
• Volatility prediction (Hillebrand & M. C. Medeiros 2010)
• Small datasets – few observations (Langella 2010)
• With other approaches e.g. feature selection – PCA (Lin and
Zhu 2007)
Citation results for publications on bagging for time series
•–
Bootstrapping (Efron ,1979) Cross validation (Stone, 1974)
Goal Estimating generalisation error Estimating generalisation error
Motivation
Cross validation aggregation for forecasting Motivation 2
Procedure Random sampling with replacement from a single learning set (bootstrap samples). The validation set is the same as the original learning set.
Splits the data into mutually exclusive subsets, using one subset as a set to train each model, and the remaining part as a validation sample (Arlot & Celisse, 2010)
Properties Low variance but is downward biased (Efron and Tibshirani, 1997)
Generalization error estimate is nearly unbiased but can be highly variable (Efron and Tibshirani, 1997)
Forecast aggregation
Bagging (Breiman 1996) – aggregates the outputs of models trained on bootstrap samples
•
•
•
•
•
Research gap:In contrast to bootstrapping, cross-validation has not been used for forecasts aggregation
•–
Bootstrapping (Efron ,1979) Cross validation (Stone, 1974)
Goal Estimating generalisation error Estimating generalisation error
Motivation
Cross validation aggregation for forecasting Motivation 2
Procedure Random sampling with replacement from a single learning set (bootstrap samples). The validation set is the same as the original learning set.
Splits the data into mutually exclusive subsets, using one subset as a set to train each model, and the remaining part as a validation sample (Arlot & Celisse, 2010)
Properties Low variance but is downward biased (Efron and Tibshirani, 1997)
Generalization error estimate is nearly unbiased but can be highly variable (Efron and Tibshirani, 1997)
Research contribution: We propose to combine the benefits of cross-validation and forecast aggregation – Crogging
Forecast aggregation
Bagging (Breiman 1996) – aggregates the outputs of models trained on bootstrap samples
•
•
•
•
•
Research gap:In contrast to bootstrapping, cross-validation has not been used for forecasts aggregation
Motivation: The Bagging algorithm
Cross validation aggregation for forecasting Motivation 3
• Inputs: learning set • Selection the number of bootstraps =
NN yyyS ,x,...,,x,,x 2211K
Motivation: The Bagging algorithm
Cross validation aggregation for forecasting Motivation 3
• Inputs: learning set • Selection the number of bootstraps =• For i=1 to K {
– Generate a bootstrap sample using (your favorite bootstrap method)
SkS
NN yyyS ,x,...,,x,,x 2211K
Motivation: The Bagging algorithm
Cross validation aggregation for forecasting Motivation 3
• Inputs: learning set • Selection the number of bootstraps =• For i=1 to K {
– Generate a bootstrap sample using (your favorite bootstrap method)– Using training set estimate a model such that }
xˆ km iik ym xˆ
SkS
kS
NN yyyS ,x,...,,x,,x 2211K
Motivation: The Bagging algorithm
Cross validation aggregation for forecasting Motivation 3
• Inputs: learning set • Selection the number of bootstraps =• For i=1 to K {
– Generate a bootstrap sample using (your favorite bootstrap method)– Using training set estimate a model such that }
xˆ km iik ym xˆ
SkS
kS
NN yyyS ,x,...,,x,,x 2211K
Motivation: The Bagging algorithm
Cross validation aggregation for forecasting Motivation 3
• Inputs: learning set • Selection the number of bootstraps =• For i=1 to K {
– Generate a bootstrap sample using (your favorite bootstrap method)– Using training set estimate a model such that }
xˆ km iik ym xˆ
SkS
kS
NN yyyS ,x,...,,x,,x 2211K
Motivation: The Bagging algorithm
Cross validation aggregation for forecasting Motivation 3
• Inputs: learning set • Selection the number of bootstraps =• For i=1 to K {
– Generate a bootstrap sample using (your favorite bootstrap method)– Using training set estimate a model such that }
• Combine model to obtain: xˆ km iik ym xˆ
K
kkmK
M1
xˆ1
xˆ
SkS
kS
NN yyyS ,x,...,,x,,x 2211K
1. Motivation2. Cross-validation and model selection3. Cross-validation aggregation4. Empirical evaluation5. Conclusions and future work
Outline
Cross validation aggregation for forecasting Cross-validation 4
• Cross validation is a widely used strategy:– Estimating the predictive accuracy of a model– Performing model selection e.g.:
• Choosing among variables in a regression or the degrees of freedom of a nonparametric model (selection for identification)
• Parameter estimation and tuning (selection for estimation)
Cross validation aggregation for forecasting Cross-validation 5
Cross-validation: Background
• Cross validation is a widely used strategy:– Estimating the predictive accuracy of a model– Performing model selection e.g.:
• Choosing among variables in a regression or the degrees of freedom of a nonparametric model (selection for identification)
• Parameter estimation and tuning (selection for estimation)
• Main features:– Main idea: test the model on data not used in estimation– Split data once or several times– Part of data is used for training each model (the training
sample), and the remaining part is used for estimating the prediction error of the model (the validation sample)
Cross validation aggregation for forecasting Cross-validation 5
Cross-validation: Background
• K-fold cross-validation:
Cross-validation: How it works?
• K-fold cross-validation:Sample 1 Sample 2 Sample K-1 Sample K
K samples (one or more observations)
Cross-validation: How it works?
• K-fold cross-validation:Sample 1 Sample 2 Sample K-1 Sample K
Estimation ValidationK samples (one or more observations)
Cross-validation: How it works?
• K-fold cross-validation:Sample 1 Sample 2 Sample K-1 Sample K
Estimation ValidationK samples (one or more observations)
Cross-validation: How it works?
• K-fold cross-validation:Sample 1 Sample 2 Sample K-1 Sample K
Estimation ValidationK samples (one or more observations)
Cross-validation: How it works?
• K-fold cross-validation:Sample 1 Sample 2 Sample K-1 Sample K
Estimation ValidationK samples (one or more observations)
Cross-validation: How it works?
• K-fold cross-validation:Sample 1 Sample 2 Sample K-1 Sample K
Estimation Validation
…
K
ti m e s
K samples (one or more observations)
Cross-validation: How it works?
• k-fold cross-validation– Divides the data into k none-overlapping and mutually exclusive
sub-samples of approximately equal size.
Cross-validation strategies
Cross validation aggregation for forecasting Cross-validation aggregation 7
• k-fold cross-validation– Divides the data into k none-overlapping and mutually exclusive
sub-samples of approximately equal size.– If k=2, 2-Fold cross validation– If k=10, 10-Fold cross validation
Cross-validation strategies
Cross validation aggregation for forecasting Cross-validation aggregation 7
• k-fold cross-validation– Divides the data into k none-overlapping and mutually exclusive
sub-samples of approximately equal size.– If k=2, 2-Fold cross validation– If k=10, 10-Fold cross validation
• If k=N, Leave-one-out cross-validation (LOOCV)
Cross-validation strategies
Cross validation aggregation for forecasting Cross-validation aggregation 7
• k-fold cross-validation– Divides the data into k none-overlapping and mutually exclusive
sub-samples of approximately equal size.– If k=2, 2-Fold cross validation– If k=10, 10-Fold cross validation
• If k=N, Leave-one-out cross-validation (LOOCV)• Monte-carlo cross-validation
– Randomly split the data into two sub-samples (training and validation) multiple times, each time randomly drawing without replacement
Cross-validation strategies
Cross validation aggregation for forecasting Cross-validation aggregation 7
• k-fold cross-validation– Divides the data into k none-overlapping and mutually exclusive
sub-samples of approximately equal size.– If k=2, 2-Fold cross validation– If k=10, 10-Fold cross validation
• If k=N, Leave-one-out cross-validation (LOOCV)• Monte-carlo cross-validation
– Randomly split the data into two sub-samples (training and validation) multiple times, each time randomly drawing without replacement
• Hold-out method– A single split into two data sub-samples
Cross-validation strategies
Cross validation aggregation for forecasting Cross-validation aggregation 7
• Goal: select a model having the smallest generalisation error
Cross validation: model selection
Cross validation aggregation for forecasting Cross-validation 8
• Goal: select a model having the smallest generalisation error• Compute an approximation of the generalisation error
defined as follows:
N
i
ii
Ngen N
mymE
1
2xˆlim
Cross validation: model selection
Cross validation aggregation for forecasting Cross-validation 8
• Goal: select a model having the smallest generalisation error• Compute an approximation of the generalisation error defined
as follows:
• Estimate model m on the training set, and calculate the error on the validation set for sample k is:
N
i
ii
Ngen N
mymE
1
2xˆlim
KN
mymE
KN
i
vali
vali
k
1
2xˆ
Cross validation: model selection
Cross validation aggregation for forecasting Cross-validation 8
• Goal: select a model having the smallest generalisation error• Compute an approximation of the generalisation error defined
as follows:
• Estimate model m on the training set, and calculate the error on the validation set for sample k is:
• Estimate the generalisation error after K repetitions as the average error across all repetitions:
N
i
ii
Ngen N
mymE
1
2xˆlim
KN
mymE
KN
i
vali
vali
k
1
2xˆ
K
mEmE
K
kk
gen
1ˆ
Cross validation: model selection
Cross validation aggregation for forecasting Cross-validation 8
• Goal: select a model having the smallest generalisation error• Compute an approximation of the generalisation error defined
as follows:
• Estimate model m on the training set, and calculate the error on the validation set for sample k is:
• Estimate the generalisation error after K repetitions as the average error across all repetitions:
N
i
ii
Ngen N
mymE
1
2xˆlim
KN
mymE
KN
i
vali
vali
k
1
2xˆ
K
mEmE
K
kk
gen
1ˆ
Cross validation: model selection
Cross validation aggregation for forecasting Cross-validation 8
Select the model with the smallest generalisation error
• Goal: select a model having the smallest generalisation error• Compute an approximation of the generalisation error defined
as follows:
• Estimate model m on the training set, and calculate the error on the validation set for sample k is:
• Estimate the generalisation error after K repetitions as the average error across all repetitions:
N
i
ii
Ngen N
mymE
1
2xˆlim
KN
mymE
KN
i
vali
vali
k
1
2xˆ
K
mEmE
K
kk
gen
1ˆ
What about the K models estimated on the different data sets?
Cross validation: model selection
Cross validation aggregation for forecasting Cross-validation 8
Select the model with the smallest generalisation error
• Goal: select a model having the smallest generalisation error• Compute an approximation of the generalisation error defined
as follows:
• Estimate model m on the training set, and calculate the error on the validation set for sample k is:
• Estimate the generalisation error after K repetitions as the average error across all repetitions:
N
i
ii
Ngen N
mymE
1
2xˆlim
KN
mymE
KN
i
vali
vali
k
1
2xˆ
K
mEmE
K
kk
gen
1ˆ
What about the K models estimated on the different data sets?
Cross validation: model selection
Cross validation aggregation for forecasting Cross-validation 8
Select the model with the smallest generalisation error
1. Motivation2. Cross-validation and model selection3. Cross-validation aggregation4. Empirical evaluation5. Conclusions and future work
Outline
Cross validation aggregation for forecasting Cross-validation aggregation 9
• In model selection, the model obtained is the one built on all the data (no data reserved for validation)– However predictive accuracy is adjudged on models built on different parts of
the data– These supplementary models are thrown away after they have served their
purpose
Cross-validation aggregation: Crogging
Cross validation aggregation for forecasting Cross-validation aggregation 10
• In model selection, the model obtained is the one built on all the data (no data reserved for validation)– However predictive accuracy is adjudged on models built on different parts of
the data– These supplementary models are thrown away after they have served their
purpose• The proposed approach:
Cross-validation aggregation: Crogging
Cross validation aggregation for forecasting Cross-validation aggregation 10
• In model selection, the model obtained is the one built on all the data (no data reserved for validation)– However predictive accuracy is adjudged on models built on different parts of
the data– These supplementary models are thrown away after they have served their
purpose• The proposed approach:
– We save the predictions made by the K estimated models
Cross-validation aggregation: Crogging
Cross validation aggregation for forecasting Cross-validation aggregation 10
• In model selection, the model obtained is the one built on all the data (no data reserved for validation)– However predictive accuracy is adjudged on models built on different parts of
the data– These supplementary models are thrown away after they have served their
purpose• The proposed approach:
– We save the predictions made by the K estimated models – This gives us a prediction for every observation in the training sample derived
from a model that was built when that observation was in the validation sample
Cross-validation aggregation: Crogging
Cross validation aggregation for forecasting Cross-validation aggregation 10
• In model selection, the model obtained is the one built on all the data (no data reserved for validation)– However predictive accuracy is adjudged on models built on different parts of
the data– These supplementary models are thrown away after they have served their
purpose• The proposed approach:
– We save the predictions made by the K estimated models – This gives us a prediction for every observation in the training sample derived
from a model that was built when that observation was in the validation sample
– We then average across the predictions from the K models to produce a final prediction.
K
ktkt m
KM
1
xˆ1
xˆ
Cross-validation aggregation: Crogging
Cross validation aggregation for forecasting Cross-validation aggregation 10
• In model selection, the model obtained is the one built on all the data (no data reserved for validation)– However predictive accuracy is adjudged on models built on different parts of
the data– These supplementary models are thrown away after they have served their
purpose• The proposed approach:
– We save the predictions made by the K estimated models – This gives us a prediction for every observation in the training sample derived
from a model that was built when that observation was in the validation sample
– We then average across the predictions from the K models to produce a final prediction.
– In the case of neural networks, we also use the validation samples for early stop training
K
ktkt m
KM
1
xˆ1
xˆ
Cross-validation aggregation: Crogging
Cross validation aggregation for forecasting Cross-validation aggregation 10
• In model selection, the model obtained is the one built on all the data (no data reserved for validation)– However predictive accuracy is adjudged on models built on different parts of
the data– These supplementary models are thrown away after they have served their
purpose• The proposed approach:
– We save the predictions made by the K estimated models – This gives us a prediction for every observation in the training sample derived
from a model that was built when that observation was in the validation sample
– We then average across the predictions from the K models to produce a final prediction.
– In the case of neural networks, we also use the validation samples for early stop training
– We average across multiple initialisations together with cross validation aggregation (to reduce variance)
K
ktkt m
KM
1
xˆ1
xˆ
Cross-validation aggregation: Crogging
Cross validation aggregation for forecasting Cross-validation aggregation 10
1. Motivation2. Cross-validation and model selection3. Cross-validation aggregation4. Empirical evaluation5. Conclusions and future work
Outline
Cross validation aggregation for forecasting Empirical evaluation 11
Complete DatasetReduced Dataset
Short Long Normal Difficult SUM
Non-Seasonal 25(NS)
25(NL)
4(NN)
3(ND) 57
Seasonal 25(SS)
25(SL)
4(SN) - 54
SUM 50 50 8 3 111
Summary description of NN3 competition time series dataset
Evaluation: Design and implementation
Cross validation aggregation for forecasting Empirical evaluation 12
•Time series data• NN3 dataset: 111 time series from the NN3 competition (Crone, Hibon, and
Nikolopoulos 2011)
20 40 60 80 100 120 1404000
5000
6000NN3_101
20 40 60 80 100 120 1400
5000
10000NN3_102
20 40 60 80 100 120 1400
5
10x 10
4 NN3_103
20 40 60 80 100 1200
5000
10000NN3_104
20 40 60 80 100 120 1402000
4000
6000NN3_105
20 40 60 80 100 120 1400
5000
10000NN3_106
20 40 60 80 100 120 1403000
4000
5000NN3_107
20 40 60 80 100 1200
5000
10000NN3_108
20 40 60 80 100 120 1400
5000
10000NN3_109
20 40 60 80 100 120 1400
1
2x 10
4 NN3_110
Plot of 10 time series from the NN3 dataset
Evaluation: Design and implementation
Cross validation aggregation for forecasting Empirical evaluation 12
•Time series data• NN3 dataset: 111 time series from the NN3 competition (Crone, Hibon, and
Nikolopoulos 2011)
Evaluation: Design and implementation
Cross validation aggregation for forecasting Empirical evaluation 12
•Time series data• NN3 dataset: 111 time series from the NN3 competition (Crone, Hibon, and
Nikolopoulos 2011)
• The following experimental setup is used:– Forecast horizon: 12 months– Holdout period: 18 months– Error Measures: SMAPE and MASE.– Rolling origin evaluation (Tashman,2000).
Evaluation: Design and implementation
Cross validation aggregation for forecasting Empirical evaluation 12
•Time series data• NN3 dataset: 111 time series from the NN3 competition (Crone, Hibon, and
Nikolopoulos 2011)
• The following experimental setup is used:– Forecast horizon: 12 months– Holdout period: 18 months– Error Measures: SMAPE and MASE.– Rolling origin evaluation (Tashman,2000).
• Neural network specification:– A univariate Multiplayer Perceptron (MLP) with Yt up to Yt-13 lags.– Each MLP network contains a single hidden layer; two hidden nodes; and a single output
node with a linear identity function. The hyperbolic tangent transfer function is used.
• Across all time series– On validation set Monte carlo cross-validation is always best– All Crogging variants outperform the benchmark Bagging algorithm
and hold-out method (NN model averaging)
Method Train Validation TestBESTMLP 1.25 0.96 1.49HOLDOUT 0.64 0.75 1.20BAG 0.76 0.70 1.21MONTECV 0.76 0.41 1.1610FOLDCV 0.69 0.45 1.072FOLDCV 0.73 0.60 1.15
Method Train Validation TestBESTMLP 12.36 11.10 17.89HOLDOUT 11.78 12.57 16.08BAG 12.95 13.17 16.32MONTECV 13.81 8.29 15.3510FOLDCV 12.65 8.94 15.522FOLDCV 13.68 11.19 15.29
MASE and SMAPE averaged over all time series on training, validation and test dataset across all time series
Evaluation: Findings
Cross validation aggregation for forecasting Empirical evaluation 13
MASE SMAPE
Boxplots of the MASE and SMAPE averaged over all ftme series for the different methods. The line of reference represents the median value of the distributions.
• Across all time series– On validation set Monte carlo cross-validation is always best– All Crogging variants outperform the benchmark Bagging algorithm
and hold-out method (NN model averaging)
Evaluation: Findings
Cross validation aggregation for forecasting Empirical evaluation 13
Length Method Forecast Horizon
1-3 4-12 13-18 1-18Long BESTMLP 10.79 16.59 20.02 16.77
HOLDOUT 9.34 14.96 16.20 14.43
BAG 9.74 15.46 16.38 14.81
MONTECV 10.86 15.16 15.43 14.54
10FOLDCV 10.39 14.04 14.82 13.69
2FOLDCV 9.03 14.64 15.69 14.06
SMAPE on test set averaged over long time series for short, medium and long forecast horizon
• Data conditions:– Long time series: 10-fold cross-validation has the smallest error for
medium to long horizons, and over forecast lead times 1-18
Evaluation: Findings
Cross validation aggregation for forecasting Empirical evaluation 14
Length Method Forecast Horizon1-3 4-12 13-18 1-18
Short BESTMLP 16.83 17.03 20.66 18.20
HOLDOUT 17.59 17.04 20.12 18.16
BAG 17.20 17.27 20.96 18.49
MONTECV 15.47 14.71 19.05 16.28
10FOLDCV 16.00 15.91 20.25 17.37
2FOLDCV 15.86 14.51 18.95 16.21
SMAPE on test set averaged over short time series for short, medium and long forecast horizon
• Data conditions:– Long time series: 10-fold cross-validation has the smallest error for
medium to long horizons, and over forecast lead times 1-18– Short time series: 2-fold cross validation and Monte-carlo cross-
validation outperform 10-fold cross-validation for all forecast horizons
Evaluation: Findings
Cross validation aggregation for forecasting Empirical evaluation 14
• Data conditions:– Long time series: 10-fold cross-validation has the smallest error for
medium to long horizons, and over forecast lead times 1-18– Short time series: 2-fold cross validation and Monte-carlo cross-
validation outperform 10-fold cross-validation for all forecast horizons
Boxplots of the SMAPE averaged across long (left) and short (right) time series
Evaluation: Findings
Cross validation aggregation for forecasting Empirical evaluation 14
Average errors Ranking all methods Ranking NN/CI
SMAPE MASE SMAPE MASE SMAPE MASE
B09 Wildi 14.84 1.13 1 2 − −
B07 Theta 14.89 1.13 2 2 − −
C27 Illies 15.18 1.25 3 9 1 7
** 2FOLDCV 15.29 1.15 4 3 2 2
** MONTECV 15.35 1.16 5 4 3 3
B03 ForecastPro 15.44 1.17 6 5 − −
… … … … … … … …** BAG 16.32 1.21 13 8 7 5
… … … … … … … …B00 AutomatANN 16.81 1.21 14 8 8 5
** MLP 17.89 1.50 15 10 9 6
• NN3 Competition:
Evaluation: Findings
Cross validation aggregation for forecasting Empirical evaluation 15
Average errors Ranking all methods Ranking NN/CI
SMAPE MASE SMAPE MASE SMAPE MASE
B09 Wildi 14.84 1.13 1 2 − −
B07 Theta 14.89 1.13 2 2 − −
C27 Illies 15.18 1.25 3 9 1 7
** 2FOLDCV 15.29 1.15 4 3 2 2
** MONTECV 15.35 1.16 5 4 3 3
B03 ForecastPro 15.44 1.17 6 5 − −
… … … … … … … …** BAG 16.32 1.21 13 8 7 5
… … … … … … … …B00 AutomatANN 16.81 1.21 14 8 8 5
** MLP 17.89 1.50 15 10 9 6
• NN3 Competition:
Evaluation: Findings
Cross validation aggregation for forecasting Empirical evaluation 15
Average errors Ranking all methods Ranking NN/CI
SMAPE MASE SMAPE MASE SMAPE MASE
B09 Wildi 14.84 1.13 1 2 − −
B07 Theta 14.89 1.13 2 2 − −
C27 Illies 15.18 1.25 3 9 1 7
** 2FOLDCV 15.29 1.15 4 3 2 2
** MONTECV 15.35 1.16 5 4 3 3
B03 ForecastPro 15.44 1.17 6 5 − −
… … … … … … … …** BAG 16.32 1.21 13 8 7 5
… … … … … … … …B00 AutomatANN 16.81 1.21 14 8 8 5
** MLP 17.89 1.50 15 10 9 6
• NN3 Competition:
Evaluation: Findings
Cross validation aggregation for forecasting Empirical evaluation 15
Average errors Ranking all methods Ranking NN/CI
SMAPE MASE SMAPE MASE SMAPE MASE
B09 Wildi 14.84 1.13 1 2 − −
B07 Theta 14.89 1.13 2 2 − −
C27 Illies 15.18 1.25 3 9 1 7
** 2FOLDCV 15.29 1.15 4 3 2 2
** MONTECV 15.35 1.16 5 4 3 3
B03 ForecastPro 15.44 1.17 6 5 − −
… … … … … … … …** BAG 16.32 1.21 13 8 7 5
… … … … … … … …B00 AutomatANN 16.81 1.21 14 8 8 5
** MLP 17.89 1.50 15 10 9 6
• NN3 Competition:
Evaluation: Findings
Cross validation aggregation for forecasting Empirical evaluation 15
1. Motivation2. Cross-validation and model selection3. Cross-validation aggregation4. Empirical evaluation5. Conclusions and future work
Outline
Cross validation aggregation for forecasting Conclusions and future work 16
Cross validation aggregation for forecasting Conclusions and future work 17
Conclusions and future work
Cross validation aggregation for forecasting Conclusions and future work 17
Conclusions and future work
Cross validation aggregation for forecasting Conclusions and future work 17
Conclusions and future work
Not a Forecasting Method!
Cross validation aggregation for forecasting Conclusions and future work 17
Conclusions and future work
Not a Forecasting Method!
A general method for improving the accuracy of a
forecast model
• Conclusion– Cross-validation aggregation outperforms model selection, Bagging
and the current approaches to model averaging which uses a single hold-out (validation sample)
Cross validation aggregation for forecasting Conclusions and future work 17
Conclusions and future work
• Conclusion– Cross-validation aggregation outperforms model selection, Bagging
and the current approaches to model averaging which uses a single hold-out (validation sample)
– It is especially effective when the amount of data available for training the model is limited as shown for short time series
Cross validation aggregation for forecasting Conclusions and future work 17
Conclusions and future work
• Conclusion– Cross-validation aggregation outperforms model selection, Bagging
and the current approaches to model averaging which uses a single hold-out (validation sample)
– It is especially effective when the amount of data available for training the model is limited as shown for short time series
– Improvements in forecast accuracy increase with forecast horizons
Cross validation aggregation for forecasting Conclusions and future work 17
Conclusions and future work
• Conclusion– Cross-validation aggregation outperforms model selection, Bagging
and the current approaches to model averaging which uses a single hold-out (validation sample)
– It is especially effective when the amount of data available for training the model is limited as shown for short time series
– Improvements in forecast accuracy increase with forecast horizons– It offers promising results on the NN3 competition
Cross validation aggregation for forecasting Conclusions and future work 17
Conclusions and future work
• Conclusion– Cross-validation aggregation outperforms model selection, Bagging
and the current approaches to model averaging which uses a single hold-out (validation sample)
– It is especially effective when the amount of data available for training the model is limited as shown for short time series
– Improvements in forecast accuracy increase with forecast horizons– It offers promising results on the NN3 competition
• Future work– Perform bias-variance decomposition and analysis– Consider other base model types other than neural networks– Evaluate forecast accuracy for a larger set of time series - M3
Competition Data (3003 time series, established benchmark)
Cross validation aggregation for forecasting Conclusions and future work 17
Conclusions and future work
Devon K. Barrow Lancaster University Management SchoolCentre for ForecastingLancaster, LA1 4YX, UKTel.: +44 (0) 7960271368Email: [email protected]
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