Data
rf1

rf1

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 river flow datasets concern the prediction of river network flows for 48 h in the future at specific locations. The dataset contains data from hourly flow observations for 8 sites in the Mississippi River network in the United States and were obtained from the US National Weather Service. Each row includes the most recent observation for each of the 8 sites as well as time-lagged observations from 6, 12, 18, 24, 36, 48 and 60 h in the past. In RF1, each site contributes 8 attribute variables to facilitate prediction. There are a total of 64 variables plus 8 target variables.The RF2 dataset extends the RF1 data by adding precipitation forecast information for each of the 8 sites (expected rainfall reported as discrete values: 0.0, 0.01, 0.25, 1.0 inches). For each observation and gauge site, the precipitation forecast for 6 h windows up to 48 h in the future is added (6, 12, 18, 24, 30, 36, 42, and 48 h). The two datasets both contain over 1 year of hourly observations ( > 9000 h) collected from September 2011 to September 2012. The domain is a natural candidate for multi-target regression because there are clear physical relationships between readings in the contiguous river network.

72 features

CHSI2_48H__0 (target)numeric523 unique values
0 missing
NASI2_48H__0 (target)numeric235 unique values
0 missing
EADM7_48H__0 (target)numeric334 unique values
0 missing
SCLM7_48H__0 (target)numeric641 unique values
0 missing
CLKM7_48H__0 (target)numeric748 unique values
0 missing
VALI2_48H__0 (target)numeric827 unique values
0 missing
NAPM7_48H__0 (target)numeric369 unique values
0 missing
DLDI4_48H__0 (target)numeric649 unique values
0 missing
CHSI2__0numeric516 unique values
0 missing
NASI2__0numeric235 unique values
0 missing
EADM7__0numeric334 unique values
0 missing
SCLM7__0numeric648 unique values
0 missing
CLKM7__0numeric747 unique values
0 missing
VALI2__0numeric824 unique values
0 missing
NAPM7__0numeric378 unique values
0 missing
DLDI4__0numeric653 unique values
0 missing
CHSI2__n6numeric516 unique values
12 missing
NASI2__n6numeric235 unique values
12 missing
EADM7__n6numeric334 unique values
12 missing
SCLM7__n6numeric648 unique values
12 missing
CLKM7__n6numeric747 unique values
12 missing
VALI2__n6numeric823 unique values
12 missing
NAPM7__n6numeric378 unique values
12 missing
DLDI4__n6numeric653 unique values
12 missing
CHSI2__n12numeric516 unique values
24 missing
NASI2__n12numeric235 unique values
24 missing
EADM7__n12numeric334 unique values
24 missing
SCLM7__n12numeric648 unique values
24 missing
CLKM7__n12numeric747 unique values
24 missing
VALI2__n12numeric822 unique values
24 missing
NAPM7__n12numeric378 unique values
24 missing
DLDI4__n12numeric653 unique values
24 missing
CHSI2__n18numeric516 unique values
36 missing
NASI2__n18numeric235 unique values
36 missing
EADM7__n18numeric334 unique values
36 missing
SCLM7__n18numeric648 unique values
36 missing
CLKM7__n18numeric747 unique values
36 missing
VALI2__n18numeric820 unique values
36 missing
NAPM7__n18numeric378 unique values
36 missing
DLDI4__n18numeric653 unique values
36 missing
CHSI2__n24numeric515 unique values
48 missing
NASI2__n24numeric235 unique values
48 missing
EADM7__n24numeric334 unique values
48 missing
SCLM7__n24numeric648 unique values
48 missing
CLKM7__n24numeric747 unique values
48 missing
VALI2__n24numeric818 unique values
48 missing
NAPM7__n24numeric378 unique values
48 missing
DLDI4__n24numeric653 unique values
48 missing
CHSI2__n36numeric515 unique values
72 missing
NASI2__n36numeric235 unique values
72 missing
EADM7__n36numeric334 unique values
72 missing
SCLM7__n36numeric648 unique values
72 missing
CLKM7__n36numeric745 unique values
72 missing
VALI2__n36numeric818 unique values
72 missing
NAPM7__n36numeric378 unique values
72 missing
DLDI4__n36numeric653 unique values
72 missing
CHSI2__n48numeric513 unique values
96 missing
NASI2__n48numeric235 unique values
96 missing
EADM7__n48numeric334 unique values
96 missing
SCLM7__n48numeric648 unique values
96 missing
CLKM7__n48numeric745 unique values
96 missing
VALI2__n48numeric818 unique values
96 missing
NAPM7__n48numeric377 unique values
96 missing
DLDI4__n48numeric653 unique values
96 missing
CHSI2__n60numeric508 unique values
120 missing
NASI2__n60numeric235 unique values
120 missing
EADM7__n60numeric334 unique values
120 missing
SCLM7__n60numeric648 unique values
120 missing
CLKM7__n60numeric744 unique values
120 missing
VALI2__n60numeric818 unique values
120 missing
NAPM7__n60numeric377 unique values
120 missing
DLDI4__n60numeric653 unique values
120 missing

62 properties

9125
Number of instances (rows) of the dataset.
72
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
3264
Number of missing values in the dataset.
120
Number of instances with at least one value missing.
72
Number of numeric attributes.
0
Number of nominal attributes.
0.95
First quartile of skewness among attributes of the numeric type.
74.06
Mean of means among attributes of the numeric type.
10.77
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.
0.76
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.
63.46
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.
9.31
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.
29.74
Mean standard deviation of attributes of the numeric type.
1.17
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.
27.85
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
0.25
Minimum kurtosis among attributes of the numeric type.
1.32
Percentage of instances having missing values.
Third quartile of entropy among attributes.
5534.3
Maximum kurtosis among attributes of the numeric type.
3.49
Minimum of means among attributes of the numeric type.
0.5
Percentage of missing values.
3.53
Third quartile of kurtosis among attributes of the numeric type.
164.62
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.
139.21
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.
0.85
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
1.93
Third quartile of skewness among attributes of the numeric type.
65.94
Maximum skewness among attributes of the numeric type.
0.89
Minimum standard deviation of attributes of the numeric type.
0.33
First quartile of kurtosis among attributes of the numeric type.
50.7
Third quartile of standard deviation of attributes of the numeric type.
61.81
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.
20.96
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.
689.52
Mean kurtosis among attributes of the numeric type.

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