Data
Weather-Versuchsbeete

Weather-Versuchsbeete

active ARFF Creative Commons Attribution 4.0 International Visibility: public Uploaded 25-06-2024 by Bruno Belucci Teixeira
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Weather measures from Versuchsbeete provided by the Max-Planck-Institute for Biogeochemistry Several weather measures provided by Max-Planck-Institute for Biogeochemistry from the Weather Station on Top of the Roof of the Institute Building. We have assembled all the files available as of 24-05-2024 on https://www.bgc-jena.mpg.de/wetter/weather_data.html There are 35 columns: id_series: The id of the time series. date: The date of the time series in the format "%Y-%m-%d %H:%M:%S". time_step: The time step on the time series. value_X (X from 0 to 20): The values of the time series, which will be used for the forecasting task. Preprocessing: 1 - Renamed column 'Date Time' to 'date' 2 - Parsed the date with the format '%d.%m.%Y %H:%M:%S' and converted it to string with format %Y-%m-%d %H:%M:%S. 3 - Replaced values of -9999 to nan. Values of -9999 seems to indicate a problem with the measure. Besides, it seems that the measure for 'ppt (mm)' started to be recorded on 2019, before all the values were already NaN. 4 - Renamed columns [1:] to 'value_X' with X from 0 to 20. 5 - Created 'id_series' with value 0. There is only one multivariate time series. 6 - Ensured that there are no missing dates and that the frequency of the time_series is 10 minutes. Filled the missing dates with NaNs. 7 - Created 'time_step' column from 'date' and 'id_series' with increasing values from 0 to the size of the time series. 8 - Casted 'date' to str, 'time_step' to int, 'value_X' to float, and defined 'id_series' as 'category'.

36 features

id_seriesnominal1 unique values
0 missing
datestring916566 unique values
0 missing
value_0numeric8027 unique values
12908 missing
value_1numeric2897 unique values
531 missing
value_2numeric1954 unique values
531 missing
value_3numeric3512 unique values
530 missing
value_4numeric2670 unique values
531 missing
value_5numeric2200 unique values
529 missing
value_6numeric3934 unique values
531 missing
value_7numeric2636 unique values
679 missing
value_8numeric2249 unique values
529 missing
value_9numeric3466 unique values
530 missing
value_10numeric2561 unique values
529 missing
value_11numeric1923 unique values
529 missing
value_12numeric3617 unique values
2685 missing
value_13numeric2872 unique values
2678 missing
value_14numeric2750 unique values
2685 missing
value_15numeric2485 unique values
2689 missing
value_16numeric2302 unique values
2686 missing
value_17numeric2673 unique values
2688 missing
value_18numeric3170 unique values
2762 missing
value_19numeric2559 unique values
2687 missing
value_20numeric2183 unique values
2689 missing
value_21numeric2863 unique values
2688 missing
value_22numeric4111 unique values
26328 missing
value_23numeric2635 unique values
3109 missing
value_24numeric2645 unique values
2817 missing
value_25numeric2530 unique values
2688 missing
value_26numeric3422 unique values
2689 missing
value_27numeric2957 unique values
2617 missing
value_28numeric2824 unique values
2945 missing
value_29numeric2322 unique values
2689 missing
value_30numeric2300 unique values
2688 missing
value_31numeric2508 unique values
2689 missing
value_32numeric65 unique values
632998 missing
time_stepnumeric916772 unique values
0 missing

19 properties

916772
Number of instances (rows) of the dataset.
36
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
730081
Number of missing values in the dataset.
662214
Number of instances with at least one value missing.
34
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
72.23
Percentage of instances having missing values.
Average class difference between consecutive instances.
2.21
Percentage of missing values.
0
Number of attributes divided by the number of instances.
94.44
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
2.78
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.

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