MACHINE LEARNING WITH PERIODIC DATA

Embodiments of the present disclosure relate to machine learning with periodic data. According to embodiments of the present disclosure, a feature representation of an input data sample is obtained from a prediction model. First Fourier coefficients for a first component in a Fourier expansion are determined by applying the feature representation into a first mapping model, and second Fourier coefficients for a second component in the Fourier expansion are determined by applying the feature representation into a second mapping model. A Fourier expansion result is determined based on the first Fourier coefficients and the second Fourier coefficients in the Fourier expansion, and a prediction result for the input data sample is determined based on the Fourier expansion result.