Data
electricity

electricity

active ARFF Publicly available Visibility: public Uploaded 10-04-2014 by Jan van Rijn
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  • concept_drift electricity mythbusting_1 OpenML-CC18 OpenML100 study_1 study_123 study_135 study_14 study_15 study_16 study_20 study_34 study_37 study_41 study_7 study_70 study_99
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Author: M. Harries, J. Gama, A. Bifet Source: [Joao Gama](http://www.inescporto.pt/~jgama/ales/ales_5.html) - 2009 Please cite: None Electricity is a widely used dataset described by M. Harries and analyzed by J. Gama (see papers below). This data was collected from the Australian New South Wales Electricity Market. In this market, prices are not fixed and are affected by demand and supply of the market. They are set every five minutes. Electricity transfers to/from the neighboring state of Victoria were done to alleviate fluctuations. The dataset (originally named ELEC2) contains 45,312 instances dated from 7 May 1996 to 5 December 1998. Each example of the dataset refers to a period of 30 minutes, i.e. there are 48 instances for each time period of one day. Each example on the dataset has 5 fields, the day of week, the time stamp, the New South Wales electricity demand, the Victoria electricity demand, the scheduled electricity transfer between states and the class label. The class label identifies the change of the price (UP or DOWN) in New South Wales relative to a moving average of the last 24 hours (and removes the impact of longer term price trends). The data was normalized by A. Bifet. ### Attribute information * Date: date between 7 May 1996 to 5 December 1998. Here normalized between 0 and 1 * Day: day of the week (1-7) * Period: time of the measurement (1-48) in half hour intervals over 24 hours. Here normalized between 0 and 1 * NSWprice: New South Wales electricity price, normalized between 0 and 1 * NSWdemand: New South Wales electricity demand, normalized between 0 and 1 * VICprice: Victoria electricity price, normalized between 0 and 1 * VICdemand: Victoria electricity demand, normalized between 0 and 1 * transfer: scheduled electricity transfer between both states, normalized between 0 and 1 ### Relevant papers M. Harries. Splice-2 comparative evaluation: Electricity pricing. Technical report, The University of South Wales, 1999. J. Gama, P. Medas, G. Castillo, and P. Rodrigues. Learning with drift detection. In SBIA Brazilian Symposium on Artificial Intelligence, pages 286–295, 2004.

9 features

class (target)nominal2 unique values
0 missing
datenumeric933 unique values
0 missing
daynominal7 unique values
0 missing
periodnumeric48 unique values
0 missing
nswpricenumeric4089 unique values
0 missing
nswdemandnumeric5266 unique values
0 missing
vicpricenumeric3798 unique values
0 missing
vicdemandnumeric2846 unique values
0 missing
transfernumeric1878 unique values
0 missing

107 properties

45312
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
2
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
7
Number of numeric attributes.
2
Number of nominal attributes.
1118.28
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
4.5
Average number of distinct values among the attributes of the nominal type.
-0.1
First quartile of skewness among attributes of the numeric type.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
3.54
Standard deviation of the number of distinct values among attributes of the nominal type.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
12.62
Mean skewness among attributes of the numeric type.
0.04
First quartile of standard deviation of attributes of the numeric type.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.24
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.16
Mean standard deviation of attributes of the numeric type.
2.81
Second quartile (Median) of entropy among attributes.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.22
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
57.55
Percentage of instances belonging to the most frequent class.
2.81
Minimal entropy among attributes.
-0.09
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.98
Entropy of the target attribute values.
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
26075
Number of instances belonging to the most frequent class.
-1.32
Minimum kurtosis among attributes of the numeric type.
0.43
Second quartile (Median) of means among attributes of the numeric type.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.73
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.81
Maximum entropy among attributes.
0
Minimum of means among attributes of the numeric type.
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.24
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
7047.77
Maximum kurtosis among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0.16
Second quartile (Median) of skewness among attributes of the numeric type.
0.71
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.48
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.5
Maximum of means among attributes of the numeric type.
2
The minimal number of distinct values among attributes of the nominal type.
11.11
Percentage of binary attributes.
0.15
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
0
Maximum mutual information between the nominal attributes and the target attribute.
-0.17
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
2.81
Third quartile of entropy among attributes.
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
392.12
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
7
The maximum number of distinct values among attributes of the nominal type.
0.01
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
158.53
Third quartile of kurtosis among attributes of the numeric type.
0.85
Average class difference between consecutive instances.
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
78.69
Maximum skewness among attributes of the numeric type.
42.45
Percentage of instances belonging to the least frequent class.
77.78
Percentage of numeric attributes.
0.5
Third quartile of means among attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.34
Maximum standard deviation of attributes of the numeric type.
19237
Number of instances belonging to the least frequent class.
22.22
Percentage of nominal attributes.
0
Third quartile of mutual information between the nominal attributes and the target attribute.
0.24
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.81
Average entropy of the attributes.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2.81
First quartile of entropy among attributes.
9.07
Third quartile of skewness among attributes of the numeric type.
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.7
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1029.17
Mean kurtosis among attributes of the numeric type.
0.27
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.2
First quartile of kurtosis among attributes of the numeric type.
0.29
Third quartile of standard deviation of attributes of the numeric type.
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.85
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.12
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.34
Mean of means among attributes of the numeric type.
0
Average mutual information between the nominal attributes and the target attribute.
0.41
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.06
First quartile of means among attributes of the numeric type.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.24
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
0.15
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.75
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

23 tasks

73319 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: class
25374 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: precision - target_feature: class
233 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: class
228 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
198 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 33% Holdout set - target_feature: class
296 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: class
486 runs - estimation_procedure: Interleaved Test then Train - target_feature: class
0 runs - target_feature: class
1319 runs - target_feature: class
1315 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
0 runs - target_feature: class
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