以下为卖家选择提供的数据验证报告:
数据描述
This dataset contains 14 different features such as air temperature, atmospheric pressure, and humidity. These were collected every 10 minutes, beginning in 2003. For efficiency, you will use only the data collected between 2009 and 2016.
The table below shows the column names, their value formats, and their description.
Index | Features | Format | Description |
---|---|---|---|
1 | Date Time | 01.01.2009 00:10:00 | Date-time reference |
2 | p (mbar) | 996.52 | The pascal SI derived unit of pressure used to quantify internal pressure. Meteorological reports typically state atmospheric pressure in millibars. |
3 | T (degC) | -8.02 | Temperature in Celsius |
4 | Tpot (K) | 265.4 | Temperature in Kelvin |
5 | Tdew (degC) | -8.9 | Temperature in Celsius relative to humidity. Dew Point is a measure of the absolute amount of water in the air, the DP is the temperature at which the air cannot hold all the moisture in it and water condenses. |
6 | rh (%) | 93.3 | Relative Humidity is a measure of how saturated the air is with water vapor, the %RH determines the amount of water contained within collection objects. |
7 | VPmax (mbar) | 3.33 | Saturation vapor pressure |
8 | VPact (mbar) | 3.11 | Vapor pressure |
9 | VPdef (mbar) | 0.22 | Vapor pressure deficit |
10 | sh (g/kg) | 1.94 | Specific humidity |
11 | H2OC (mmol/mol) | 3.12 | Water vapor concentration |
12 | rho (g/m ** 3) | 1307.75 | Airtight |
13 | wv (m/s) | 1.03 | Wind speed |
14 | max. wv (m/s) | 1.75 | Maximum wind speed |
15 | wd (deg) | 152.3 | Wind direction in degrees |
Inspiration
This dataset can be used to apply different styles of models including Jenkins's methods or Convolutional and Recurrent Neural Networks (CNNs and RNNs).
for example:
Forecast for a single time step:
- A single feature
- All features
Forecast multiple steps:
- Single-shot: Make the predictions all at once
- Autoregressive: Make one prediction at a time and feed the output back to the model

Max Planck Weather Dataset
40.04MB
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