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Predicting multivariate time series in real time with confidence intervals: Applications to renewable energy

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Abstract

We extend our earlier work on predicting a univariate time series in real time with confidence intervals (Hirata, et al., Renew. Energy 67, 35 (2014)) to a multivariate time series. We realize this extension by using the “p-norm” where p is smaller than 1. We compare the performance when p is 0.5 with that when p is 2 using solar irradiation data and wind data measured all over Japan.

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Correspondence to Yoshito Hirata.

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Hirata, Y., Aihara, K. & Suzuki, H. Predicting multivariate time series in real time with confidence intervals: Applications to renewable energy. Eur. Phys. J. Spec. Top. 223, 2451–2460 (2014). https://doi.org/10.1140/epjst/e2014-02210-3

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  • DOI: https://doi.org/10.1140/epjst/e2014-02210-3

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