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scm1d

scm1d

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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).

296 features

LBL (target)numeric1092 unique values
0 missing
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storageCostnumeric22 unique values
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0 missing

62 properties

9803
Number of instances (rows) of the dataset.
296
Number of attributes (columns) of the dataset.
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.
296
Number of numeric attributes.
0
Number of nominal attributes.
0.48
First quartile of skewness among attributes of the numeric type.
502.24
Mean of means among attributes of the numeric type.
43.85
First quartile of standard deviation of attributes of the numeric type.
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
3.63
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.03
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
195.89
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2.91
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
117.06
Mean standard deviation of attributes of the numeric type.
1.54
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
0
Percentage of binary attributes.
59.86
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.28
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
271.93
Maximum kurtosis among attributes of the numeric type.
8.95
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
35.52
Third quartile of kurtosis among attributes of the numeric type.
1820.05
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
100
Percentage of numeric attributes.
725.6
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-0.1
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
4.2
Third quartile of skewness among attributes of the numeric type.
12.07
Maximum skewness among attributes of the numeric type.
1.65
Minimum standard deviation of attributes of the numeric type.
-0.14
First quartile of kurtosis among attributes of the numeric type.
208.49
Third quartile of standard deviation of attributes of the numeric type.
344.25
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
142.29
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
33.43
Mean kurtosis among attributes of the numeric type.

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