A new survey examines how frequency-domain techniques are being applied to time series analysis, contrasting them with conventional time-domain approaches. It outlines unresolved theoretical and practical problems in the field and discusses benchmarks and open research directions.
The paper studies the approximation and statistical learning of nonlinear functionals defined on reproducing kernel Hilbert spaces (RKHSs) using neural networks. It is motivated by functional data such as time series and images.
A new research paper addresses how to learn the gain, the scale parameter that multiplies the scaled log-likelihood score in score-driven filters, directly online. Rather than fixing this coefficient beforehand, the method treats each admissible gain as selecting a reachable next state given the current state and the realized scaled score. This allows the filter's update step to adapt during operation.