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datapm2.5

datapm2.5

active ARFF Public Domain (CC0) Visibility: public Uploaded 20-01-2020 by Tanatip Watthaisong
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The proposed forecasting approach is tested by using the database from UCI machine learning repository. Using a Deep Learning Model Based on 1D Convnets and Bidirectional GRU

10 features

Nonumeric43800 unique values
0 missing
yearnumeric5 unique values
0 missing
monthnumeric12 unique values
0 missing
daynumeric31 unique values
0 missing
hournumeric24 unique values
0 missing
pm2.5numeric581 unique values
0 missing
DEWPnumeric69 unique values
0 missing
TEMPnumeric64 unique values
0 missing
cbwdnumeric4 unique values
0 missing
Iwsnumeric2788 unique values
0 missing

19 properties

43800
Number of instances (rows) of the dataset.
10
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.
10
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

9 tasks

0 runs - estimation_procedure: Test on Training Data - evaluation_measure: Mean absolute error - target_feature: pm2.5
0 runs - estimation_procedure: Test on Training Data - evaluation_measure: predictive_accuracy - target_feature: pm2.5
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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