Data
scm20d

scm20d

active ARFF Publicly available Visibility: public Uploaded 14-03-2019 by Quay Au
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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

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
MTLp9A (target)numeric1038 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
MTLp16A (target)numeric1415 unique values
0 missing
timeunitnumeric188 unique values
0 missing
storageCostnumeric22 unique values
0 missing
interestRatenumeric7 unique values
0 missing
compidx0lt2numeric6513 unique values
0 missing
compidx4lt2numeric5921 unique values
0 missing
compidx6lt2numeric5911 unique values
0 missing
compidx8lt2numeric5703 unique values
0 missing
compidx10lt2numeric5631 unique values
0 missing
compidx12lt2numeric6002 unique values
0 missing
compidx14lt2numeric5987 unique values
0 missing
compidx0lt6numeric7709 unique values
0 missing
compidx4lt6numeric6929 unique values
0 missing
compidx6lt6numeric7082 unique values
0 missing
compidx8lt6numeric6587 unique values
0 missing
compidx10lt6numeric6593 unique values
0 missing
compidx12lt6numeric7081 unique values
0 missing
compidx14lt6numeric7178 unique values
0 missing
compidx0lt10numeric7768 unique values
0 missing
compidx4lt10numeric6977 unique values
0 missing
compidx6lt10numeric7072 unique values
0 missing
compidx8lt10numeric6653 unique values
0 missing
compidx10lt10numeric6612 unique values
0 missing
compidx12lt10numeric7097 unique values
0 missing
compidx14lt10numeric6998 unique values
0 missing
compidx0lt20numeric7852 unique values
0 missing
compidx4lt20numeric6780 unique values
0 missing
compidx6lt20numeric6849 unique values
0 missing
compidx8lt20numeric6453 unique values
0 missing
compidx10lt20numeric6543 unique values
0 missing
compidx12lt20numeric6879 unique values
0 missing
compidx14lt20numeric6930 unique values
0 missing
compidx0lt30numeric8434 unique values
0 missing
compidx4lt30numeric7744 unique values
0 missing
compidx6lt30numeric7774 unique values
0 missing
compidx8lt30numeric7333 unique values
0 missing
compidx10lt30numeric7296 unique values
0 missing
compidx12lt30numeric7772 unique values
0 missing
compidx14lt30numeric7814 unique values
0 missing
sku1numeric896 unique values
0 missing
sku10numeric980 unique values
0 missing
sku11numeric949 unique values
0 missing
sku12numeric1025 unique values
0 missing
sku13numeric1135 unique values
0 missing
sku14numeric1172 unique values
0 missing
sku15numeric1203 unique values
0 missing
sku16numeric1299 unique values
0 missing
demandseg1numeric114 unique values
0 missing
demandseg1l1numeric114 unique values
0 missing
demandseg1l2numeric114 unique values
0 missing
demandseg1l4numeric114 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

62 properties

8966
Number of instances (rows) of the dataset.
77
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.
77
Number of numeric attributes.
0
Number of nominal attributes.
0.25
First quartile of skewness among attributes of the numeric type.
580.9
Mean of means among attributes of the numeric type.
27.46
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.
-0.28
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.01
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
193.71
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.
0.88
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.
116.46
Mean standard deviation of attributes of the numeric type.
0.39
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.
46.53
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.3
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
154.98
Maximum kurtosis among attributes of the numeric type.
8.96
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
0.68
Third quartile of kurtosis among attributes of the numeric type.
1815.51
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.
1273.86
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.
0.86
Third quartile of skewness among attributes of the numeric type.
10.6
Maximum skewness among attributes of the numeric type.
1.65
Minimum standard deviation of attributes of the numeric type.
-0.98
First quartile of kurtosis among attributes of the numeric type.
233.88
Third quartile of standard deviation of attributes of the numeric type.
333.77
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.
71.48
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.
3.45
Mean kurtosis among attributes of the numeric type.

1 tasks

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