From the course: Applied AI for IT Operations (AIOps)
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Testing the time series model - Python Tutorial
From the course: Applied AI for IT Operations (AIOps)
Testing the time series model
- [Instructor] In this video, we will test the model we built earlier using the test data set. Please note that for predicting each data point with the test data set, we have the previous 168 data points from actual data. In order to test, we first process the test data set with the same create_rnn_dataset function to associate each data point with the previous 168 data points. Then we use the evaluate function to evaluate the model. For comparison sake, we will predict for both the training data set and the test data set. In order to review the results, we will need to do an inverse transform to convert them from the standard scale to the same scale as the original values. Let's run this code now. Now, let's plot the predictions obtained to see how they match up against the original data. The total size of the plot would be the combined size of the predictions on training and testing. First, we get the original data from the data frame and convert it to the same shape as that of the…
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Contents
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Time series forecasting2m 27s
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(Locked)
Recurrent neural network (RNN) and long short-term memory (LSTM)1m 37s
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(Locked)
Preparing sequence data4m 8s
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(Locked)
Building an LSTM model with Keras2m 19s
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(Locked)
Testing the time series model2m 30s
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(Locked)
Forecasting future service loads with Keras3m 10s
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