Data
dj30-1985-2003

dj30-1985-2003

deactivated ARFF Publicly available Visibility: public Uploaded 29-09-2014 by Joaquin Vanschoren
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Author: Danilo Careggio (careggio.danilo@tiscali.it) Source: [StatLib](http://lib.stat.cmu.edu/datasets/) - 6/Feb/04 Please cite: For each stock of Dow Jones 30, starting from 1985 up to and including Oct. 30th, 2003, daily quotations with open/close and adjusted close values, min./max values, volume are submitted. The said data are the result of a merge between different CSV files, downloaded from www.yahoo.com, as I made it to write my dissertation at the Dept. of Computer Science of University of Turin, Italy with Rosa Meo Prof. Data format is as follows: ID (Unique Identifier); date; open; high; low; volume; adjclose; ticker.

9 features

stock (target)nominal30 unique values
0 missing
ID (ignore)numeric138166 unique values
0 missing
datedate4756 unique values
0 missing
opennumeric10295 unique values
0 missing
highnumeric10615 unique values
0 missing
lownumeric10470 unique values
0 missing
volumenumeric10744 unique values
0 missing
tickernumeric49082 unique values
0 missing
adjclosenumeric10120 unique values
0 missing

107 properties

138166
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
30
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.
8
Number of numeric attributes.
1
Number of nominal attributes.
Second quartile (Median) of entropy among attributes.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.68
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
0.35
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
3.44
Percentage of instances belonging to the most frequent class.
24316317606.91
Mean standard deviation of attributes of the numeric type.
1.45
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
4.9
Entropy of the target attribute values.
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
4756
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
55.98
Second quartile (Median) of means among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.56
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.18
Minimum kurtosis among attributes of the numeric type.
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
0.94
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
92.66
Maximum kurtosis among attributes of the numeric type.
22.68
Minimum of means among attributes of the numeric type.
0.93
Second quartile (Median) of skewness among attributes of the numeric type.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.03
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
776378895899.15
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of binary attributes.
27.28
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.93
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.
Maximum mutual information between the nominal attributes and the target attribute.
30
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
30
The maximum number of distinct values among attributes of the nominal type.
-0.04
Minimum skewness among attributes of the numeric type.
0
Percentage of missing values.
10.92
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
7.41
Maximum skewness among attributes of the numeric type.
25.3
Minimum standard deviation of attributes of the numeric type.
88.89
Percentage of numeric attributes.
2975504.63
Third quartile of means among attributes of the numeric type.
0.9
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
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
170207204337.45
Maximum standard deviation of attributes of the numeric type.
1.83
Percentage of instances belonging to the least frequent class.
11.11
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.31
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
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
2531
Number of instances belonging to the least frequent class.
First quartile of entropy among attributes.
2.52
Third quartile of skewness among attributes of the numeric type.
0.68
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
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
15.46
Mean kurtosis among attributes of the numeric type.
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.44
First quartile of kurtosis among attributes of the numeric type.
7018776.55
Third quartile of standard deviation of attributes of the numeric type.
0.9
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
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
110911695950.04
Mean of means among attributes of the numeric type.
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
55.3
First quartile of means among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.31
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
0.14
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
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
0.68
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
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
0.93
First quartile of skewness among attributes of the numeric type.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.9
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.11
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
30
Average number of distinct values among the attributes of the nominal type.
26.98
First quartile of standard deviation of attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.31
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
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.89
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.94
Mean skewness among attributes of the numeric type.

6 tasks

0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: stock
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: stock
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: stock
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task