Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 7, issue 12
Articles | Volume 7, issue 12
https://doi.org/10.5194/amt-7-4387-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-7-4387-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 7, issue 12
Research article
 | 
11 Dec 2014
Research article |  | 11 Dec 2014

Regression models tolerant to massively missing data: a case study in solar-radiation nowcasting

I. Žliobaitė, J. Hollmén, and H. Junninen

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Short summary
We present a case study in solar/radiation nowcasting using environmental sensor measurements as inputs. While some sensor readings may oftentimes be missing, predictions need to be output continuously in near real time. We are after linear regression models that would be robust to missing data, i.e., that would perform well with or without data gaps. We recommend using regularized a PCA regression with our established guidelines for building robust regression models.
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