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keras-io / timeseries_forecasting_for_weather
Model's Last Updated: July 09 2024
time-series-forecasting
Introduction of timeseries_forecasting_for_weather
Model Details of timeseries_forecasting_for_weather
Model description
Demonstrates timeseries forecasting using a LSTM model.
Full credits to:
Data Preprocessing
Here we are picking ~300,000 data points for training. Observation is recorded every 10 mins, that means 6 times per hour. We will resample one point per hour since no drastic change is expected within 60 minutes. We do this via the sampling_rate argument in timeseries_dataset_from_array utility.
We are tracking data from past 720 timestamps (720/6=120 hours). This data will be used to predict the temperature after 72 timestamps (72/6=12 hours).
Since every feature has values with varying ranges, we do normalization to confine feature values to a range of [0, 1] before training a neural network. We do this by subtracting the mean and dividing by the standard deviation of each feature.
71.5 % of the data will be used to train the model, i.e. 300,693 rows. split_fraction can be changed to alter this percentage.
The model is shown data for first 5 days i.e. 720 observations, that are sampled every hour. The temperature after 72 (12 hours * 6 observation per hour) observation will be used as a label.
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
View Model Plot
Runs of keras-io timeseries_forecasting_for_weather on huggingface.co
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Updated:July 05 2024
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