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scm20d

scm20d

in_preparation ARFF Publicly available Visibility: public Uploaded 22-11-2018 by Rodriguez
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Multivariate regression data set from: https://link.springer.com/article/10.1007%2Fs10994-016-5546-z : The Supply Chain Management datasets are derived from the Trading Agent Competition in Supply Chain Management (TAC SCM) tournament from 2010. The precise methods for data preprocessing and normalization are described in detail by Groves and Gini (2011). Some benchmark values for prediction accuracy in this domain are available from the TAC SCM Prediction Challenge (Pardoe and Stone 2008), these datasets correspond only to the Product Future prediction type. Each row corresponds to an observation day in the tournament (there are 220 days in each game and 18 tournament games in a tournament). The input variables in this domain are observed prices for a specific tournament day. In addition, 4 time-delayed observations are included for each observed product and component (1, 2, 4 and 8 days delayed) to facilitate some anticipation of trends going forward. The datasets contain 16 regression targets, each target corresponds to the next day mean price (SCM1D) or mean price for 20-days in the future (SCM20D) for each product in the simulation. Days with no target values are excluded from the datasets (i.e. days with labels that are beyond the end of the game are excluded).

77 features

MTLp16A (target)numeric1415 unique values
0 missing
LBL (target)numeric1045 unique values
0 missing
MTLp2A (target)numeric1097 unique values
0 missing
MTLp3A (target)numeric1075 unique values
0 missing
MTLp4A (target)numeric1157 unique values
0 missing
MTLp5A (target)numeric1237 unique values
0 missing
MTLp6A (target)numeric1324 unique values
0 missing
MTLp7A (target)numeric1360 unique values
0 missing
MTLp8A (target)numeric1429 unique values
0 missing
MTLp10A (target)numeric1118 unique values
0 missing
MTLp11A (target)numeric1054 unique values
0 missing
MTLp12A (target)numeric1118 unique values
0 missing
MTLp13A (target)numeric1278 unique values
0 missing
MTLp14A (target)numeric1319 unique values
0 missing
MTLp15A (target)numeric1343 unique values
0 missing
MTLp9A (target)numeric1038 unique values
0 missing
demandseg1l4numeric114 unique values
0 missing
demandseg1l2numeric114 unique values
0 missing
demandseg1l1numeric114 unique values
0 missing
demandseg1numeric114 unique values
0 missing
sku16numeric1299 unique values
0 missing
sku15numeric1203 unique values
0 missing
sku14numeric1172 unique values
0 missing
sku13numeric1135 unique values
0 missing
sku12numeric1025 unique values
0 missing
sku11numeric949 unique values
0 missing
sku1numeric896 unique values
0 missing
sku10numeric980 unique values
0 missing
demandseg1l8numeric115 unique values
0 missing
demandseg2numeric139 unique values
0 missing
demandseg2l1numeric139 unique values
0 missing
demandseg2l2numeric139 unique values
0 missing
demandseg2l4numeric139 unique values
0 missing
demandseg2l8numeric139 unique values
0 missing
demandseg3numeric117 unique values
0 missing
demandseg3l1numeric117 unique values
0 missing
demandseg3l2numeric117 unique values
0 missing
demandseg3l4numeric118 unique values
0 missing
demandseg3l8numeric117 unique values
0 missing
compidx14lt2numeric5987 unique values
0 missing
compidx4lt10numeric6977 unique values
0 missing
compidx0lt10numeric7768 unique values
0 missing
compidx14lt6numeric7178 unique values
0 missing
compidx12lt6numeric7081 unique values
0 missing
compidx10lt6numeric6593 unique values
0 missing
compidx8lt6numeric6587 unique values
0 missing
compidx6lt6numeric7082 unique values
0 missing
compidx4lt6numeric6929 unique values
0 missing
compidx0lt6numeric7709 unique values
0 missing
compidx6lt10numeric7072 unique values
0 missing
compidx12lt2numeric6002 unique values
0 missing
compidx10lt2numeric5631 unique values
0 missing
compidx8lt2numeric5703 unique values
0 missing
compidx6lt2numeric5911 unique values
0 missing
compidx4lt2numeric5921 unique values
0 missing
compidx0lt2numeric6513 unique values
0 missing
interestRatenumeric7 unique values
0 missing
storageCostnumeric22 unique values
0 missing
compidx10lt20numeric6543 unique values
0 missing
compidx14lt30numeric7814 unique values
0 missing
compidx12lt30numeric7772 unique values
0 missing
compidx10lt30numeric7296 unique values
0 missing
compidx8lt30numeric7333 unique values
0 missing
compidx6lt30numeric7774 unique values
0 missing
compidx4lt30numeric7744 unique values
0 missing
compidx0lt30numeric8434 unique values
0 missing
compidx14lt20numeric6930 unique values
0 missing
compidx12lt20numeric6879 unique values
0 missing
timeunitnumeric188 unique values
0 missing
compidx8lt20numeric6453 unique values
0 missing
compidx6lt20numeric6849 unique values
0 missing
compidx4lt20numeric6780 unique values
0 missing
compidx0lt20numeric7852 unique values
0 missing
compidx14lt10numeric6998 unique values
0 missing
compidx12lt10numeric7097 unique values
0 missing
compidx10lt10numeric6612 unique values
0 missing
compidx8lt10numeric6653 unique values
0 missing
Show all 77 features

62 properties

NumberOfInstances
8966
Number of instances (rows) of the dataset.
NumberOfFeatures
77
Number of attributes (columns) of the dataset.
NumberOfClasses
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
77
Number of numeric attributes.
NumberOfSymbolicFeatures
0
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
100
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.98
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
71.48
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
0.25
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
27.46
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.28
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
193.71
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.39
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
46.53
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.68
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
1273.86
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.86
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
233.88
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
580.9
Mean of means among attributes of the numeric type.
ClassEntropy
Entropy of the target attribute values.
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MajorityClassPercentage
Percentage of instances belonging to the most frequent class.
MajorityClassSize
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
154.98
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1815.51
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
10.6
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
333.77
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
3.45
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
0
Number of binary attributes.
MeanMutualInformation
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.88
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
116.46
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-1.3
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
8.96
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.1
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
1.65
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
Percentage of instances belonging to the least frequent class.
MinorityClassSize
Number of instances belonging to the least frequent class.
Show all 62 properties

9 tasks

Clustering on scm20d
0 runs - estimation_procedure: 50 times Clustering
Clustering on scm20d
0 runs - estimation_procedure: 50 times Clustering
Clustering on scm20d
0 runs - estimation_procedure: 50 times Clustering
Clustering on scm20d
0 runs - estimation_procedure: 50 times Clustering
Clustering on scm20d
0 runs - estimation_procedure: 50 times Clustering
Clustering on scm20d
0 runs - estimation_procedure: 50 times Clustering
Clustering on scm20d
0 runs - estimation_procedure: 50 times Clustering
Clustering on scm20d
0 runs - estimation_procedure: 50 times Clustering
Clustering on scm20d
0 runs - estimation_procedure: 50 times Clustering
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