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DeployableTime Series Prediction
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By IBM Developer Staff | Published September 21, 2018
Artificial IntelligenceTime Series Prediction
This model takes hourly weather data (as a Numpy array of various weather features, in text file format) as input and returns hourly weather predictions for a specific target variable or variables (such as temperature or windspeed).
Three pre-trained models are provided, all trained by the CODAIT team on National Oceanic and Atmospheric Administration local climatological data originally collected by JFK airport. All three models use an LSTM recurrent neural network architecture.
A description of the weather variables used to train the models is set out below.
For further details on the weather variables see the US Local Climatological Data Documentation
Each model returns a different format for its predictions:
Literature and Documentation
This model can be deployed using the following mechanisms:
docker run -it -p 5000:5000 codait/max-weather-forecaster
kubectl apply -f https://raw.githubusercontent.com/IBM/MAX-Weather-Forecaster/master/max-weather-forecaster.yaml
Once deployed, you can test the model from the command line. For example to test the multi-step model when running locally:
curl -F "file=@assets/lstm_weather_test_data/multistep_model_test_data.txt" -XPOST http://localhost:5000/model/predict?model=multistep
You should see a JSON response like that below for the multistep test data, where predictions contains the predicted dry bulb temperature (in F) for each of the next 48 hours, for each input data point.
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