From the course: Applied AI for IT Operations (AIOps)
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Preparing sequence data - Python Tutorial
From the course: Applied AI for IT Operations (AIOps)
Preparing sequence data
- [Instructor] In this video, we will explore and prepare the time series data for deep learning. We want to predict the service request loads for the future, and we will use the service request loads in the past as training data. The training data is available in the file request_every_hour.csv. Each record contains the total request for a given hour. The file is contains continuous hourly sample points for a period of five weeks. The notebook for this chapter is available in code_04_XX Forecasting Service Loads. Let's open the notebook. We first make sure that all the dependent packages are installed. If not, install them. First, we load up the CSV containing the time series and explore its content. There is only one column containing the request. This is both the feature and the target variable for our machine learning. We are using the past request loads to predict the future request loads. Let's run this code and review the results. The data is successfully loaded now. Next…
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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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