Data
ozone-level-8hr

ozone-level-8hr

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
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  • OpenML-CC18 OpenML100 study_123 study_14 study_34 study_50 study_52 study_7 study_98 study_99
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Author: Kun Zhang, Wei Fan, XiaoJing Yuan Source: [UCI](https://archive.ics.uci.edu/ml/datasets/ozone+level+detection) Please cite: Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond, Knowledge and Information Systems, Vol. 14, No. 3, 2008. 1 . Abstract: Two ground ozone level data sets are included in this collection. One is the eight hour peak set (eighthr.data), the other is the one hour peak set (onehr.data). Those data were collected from 1998 to 2004 at the Houston, Galveston and Brazoria area. 2. Source: Kun Zhang, zhang.kun05 '@' gmail.com, Department of Computer Science, Xavier University of Lousiana Wei Fan, wei.fan '@' gmail.com, IBM T.J.Watson Research XiaoJing Yuan, xyuan '@' uh.edu, Engineering Technology Department, College of Technology, University of Houston 3. Data Set Information: All the attribute start with T means the temperature measured at different time throughout the day; and those starts with WS indicate the wind speed at various time. WSR_PK: continuous. peek wind speed -- resultant (meaning average of wind vector) WSR_AV: continuous. average wind speed T_PK: continuous. Peak T T_AV: continuous. Average T T85: continuous. T at 850 hpa level (or about 1500 m height) RH85: continuous. Relative Humidity at 850 hpa U85: continuous. (U wind - east-west direction wind at 850 hpa) V85: continuous. V wind - N-S direction wind at 850 HT85: continuous. Geopotential height at 850 hpa, it is about the same as height at low altitude T70: continuous. T at 700 hpa level (roughly 3100 m height) RH70: continuous. U70: continuous. V70: continuous. HT70: continuous. T50: continuous. T at 500 hpa level (roughly at 5500 m height) RH50: continuous. U50: continuous. V50: continuous. HT50: continuous. KI: continuous. K-Index [Web Link] TT: continuous. T-Totals [Web Link] SLP: continuous. Sea level pressure SLP_: continuous. SLP change from previous day Precp: continuous. -- precipitation 4. Attribute Information: The following are specifications for several most important attributes that are highly valued by Texas Commission on Environmental Quality (TCEQ). More details can be found in the two relevant papers. O 3 - Local ozone peak prediction Upwind - Upwind ozone background level EmFactor - Precursor emissions related factor Tmax - Maximum temperature in degrees F Tb - Base temperature where net ozone production begins (50 F) SRd - Solar radiation total for the day WSa - Wind speed near sunrise (using 09-12 UTC forecast mode) WSp - Wind speed mid-day (using 15-21 UTC forecast mode) 5. Relevant Papers: Forecasting skewed biased stochastic ozone days: analyses, solutions and beyond, Knowledge and Information Systems, Vol. 14, No. 3, 2008. It Discusses details about the dataset, its use as well as various experiments (both cross-validation and streaming) using many state-of-the-art methods. A shorter version of the paper (does not contain some detailed experiments as the journal paper above) is in: Forecasting Skewed Biased Stochastic Ozone Days: Analyses and Solutions. ICDM 2006: 753-764

73 features

Class (target)nominal2 unique values
0 missing
V1numeric69 unique values
0 missing
V2numeric71 unique values
0 missing
V3numeric66 unique values
0 missing
V4numeric67 unique values
0 missing
V5numeric65 unique values
0 missing
V6numeric64 unique values
0 missing
V7numeric67 unique values
0 missing
V8numeric68 unique values
0 missing
V9numeric70 unique values
0 missing
V10numeric71 unique values
0 missing
V11numeric77 unique values
0 missing
V12numeric78 unique values
0 missing
V13numeric78 unique values
0 missing
V14numeric79 unique values
0 missing
V15numeric78 unique values
0 missing
V16numeric79 unique values
0 missing
V17numeric73 unique values
0 missing
V18numeric74 unique values
0 missing
V19numeric71 unique values
0 missing
V20numeric66 unique values
0 missing
V21numeric69 unique values
0 missing
V22numeric70 unique values
0 missing
V23numeric69 unique values
0 missing
V24numeric66 unique values
0 missing
V25numeric75 unique values
0 missing
V26numeric56 unique values
0 missing
V27numeric283 unique values
0 missing
V28numeric285 unique values
0 missing
V29numeric288 unique values
0 missing
V30numeric284 unique values
0 missing
V31numeric284 unique values
0 missing
V32numeric292 unique values
0 missing
V33numeric296 unique values
0 missing
V34numeric312 unique values
0 missing
V35numeric314 unique values
0 missing
V36numeric315 unique values
0 missing
V37numeric328 unique values
0 missing
V38numeric331 unique values
0 missing
V39numeric335 unique values
0 missing
V40numeric336 unique values
0 missing
V41numeric336 unique values
0 missing
V42numeric340 unique values
0 missing
V43numeric338 unique values
0 missing
V44numeric330 unique values
0 missing
V45numeric322 unique values
0 missing
V46numeric307 unique values
0 missing
V47numeric303 unique values
0 missing
V48numeric295 unique values
0 missing
V49numeric288 unique values
0 missing
V50numeric285 unique values
0 missing
V51numeric331 unique values
0 missing
V52numeric297 unique values
0 missing
V53numeric252 unique values
0 missing
V54numeric101 unique values
0 missing
V55numeric1289 unique values
0 missing
V56numeric1462 unique values
0 missing
V57numeric369 unique values
0 missing
V58numeric246 unique values
0 missing
V59numeric101 unique values
0 missing
V60numeric1538 unique values
0 missing
V61numeric1430 unique values
0 missing
V62numeric442 unique values
0 missing
V63numeric187 unique values
0 missing
V64numeric101 unique values
0 missing
V65numeric1688 unique values
0 missing
V66numeric1510 unique values
0 missing
V67numeric86 unique values
0 missing
V68numeric1048 unique values
0 missing
V69numeric658 unique values
0 missing
V70numeric72 unique values
0 missing
V71numeric57 unique values
0 missing
V72numeric175 unique values
0 missing

19 properties

2534
Number of instances (rows) of the dataset.
73
Number of attributes (columns) of the dataset.
2
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.
72
Number of numeric attributes.
1
Number of nominal attributes.
1.37
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0.92
Average class difference between consecutive instances.
98.63
Percentage of numeric attributes.
0.03
Number of attributes divided by the number of instances.
1.37
Percentage of nominal attributes.
93.69
Percentage of instances belonging to the most frequent class.
2374
Number of instances belonging to the most frequent class.
6.31
Percentage of instances belonging to the least frequent class.
160
Number of instances belonging to the least frequent class.
1
Number of binary attributes.

16 tasks

90640 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
81656 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
45 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - target_feature: Class
1310 runs - target_feature: Class
1302 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
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