metadata
library_name: keras
tags:
- time series
Model description
Demonstrates timeseries forecasting using a LSTM model.
Full credits to:
Intended uses & limitations
More information needed
Training and evaluation data
We will be using Jena Climate dataset recorded by the Max Planck Institute for Biogeochemistry. The dataset consists of 14 features such as temperature, pressure, humidity etc, recorded once per 10 minutes.
Location: Weather Station, Max Planck Institute for Biogeochemistry in Jena, Germany.
Time-frame Considered: Jan 10, 2009 - December 31, 2016
The table below shows the column names, their value formats, and their description.
| Index | Features | Format | Description | Selected Features |
|---|---|---|---|---|
| 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 | - |
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision |
|---|---|---|---|---|---|---|---|
| Adam | 0.0010000000474974513 | 0.0 | 0.8999999761581421 | 0.9990000128746033 | 1e-07 | False | float32 |
Model Plot
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