Scalable Forecasting Techniques Applied to Big Electricity Time Series

@inproceedings{Galicia2017ScalableFT,
  title={Scalable Forecasting Techniques Applied to Big Electricity Time Series},
  author={Antonio Galicia and Jos{\'e} F. Torres and Francisco Mart{\'i}nez-{\'A}lvarez and Alicia Troncoso Lora},
  booktitle={IWANN},
  year={2017}
}
This paper presents different scalable methods to predict time series of very long length such as time series with a high sampling frequency. The Apache Spark framework for distributed computing is proposed in order to achieve the scalability of the methods. Namely, the existing MLlib machine learning library from Spark has been used. Since MLlib does not support multivariate regression, the forecasting problem has been split into h forecasting subproblems, where h is the number of future… 

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