Chapter 7. Improving your company’s monthly power usage forecast
This chapter covers
- Adding additional data to your analysis
- Using pandas to fill in missing values
- Visualizing your time-series data
- Using a neural network to generate forecasts
- Using DeepAR to forecast power consumption
In chapter 6, you worked with Kiara to develop an AWS SageMaker DeepAR model to predict power consumption across her company’s 48 sites. You had just a bit more than one year’s data for each of the sites, and you predicted the temperature for November 2018 with an average percentage error of less that 6%. Amazing! Let’s expand on this scenario by adding additional data for our analysis and filling in any missing values. First, let’s take a deeper look at DeepAR. ...
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