Notebook
scikit-learn-pythonmldata-sciencesolutionmachinelearningmicrosoft-for-beginnersmachine-learning1-Introductionmachinelearning-pythoneducationmicrosoft-ML-For-Beginnersscikit-learnPythonmachine-learning-algorithms7-TimeSeriesr
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Data Setup
In this notebook, we demonstrate how to:
- setup time series data for this module
- visualize the data
The data in this example is taken from the GEFCom2014 forecasting competition1. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014.
1Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016.
[6]
Load the data from csv into a Pandas dataframe
[7]
Plot all available load data (January 2012 to Dec 2014)
[8]
Plot first week of July 2014
[9]
