  In machine learning, a nonparametric forecasting algorithm for time series
data has been proposed, called the kernel spectral hidden Markov model (KSHMM).
In this paper, we propose a technique for short-term wind-speed prediction
based on KSHMM. We numerically compared the performance of our KSHMM-based
forecasting technique to other techniques with machine learning, using
wind-speed data offered by the National Renewable Energy Laboratory. Our
results demonstrate that, compared to these methods, the proposed technique
offers comparable or better performance.
