Deep Learning Accelerates Spectral Density Estimation for Functional Time Series
Analysis
This paper presents a novel deep learning approach to address the computational bottleneck in spectral density estimation for functional time series, particularly those defined on large domains. By circumventing the need to compute large autocovariance kernels, the proposed method offers a significant speedup and enables analysis of datasets previously intractable. The application to fMRI images demonstrates the practical relevance and potential impact of this technique.
Key Takeaways
- •Proposes a deep learning estimator for spectral density of functional time series.
- •Avoids computation of large autocovariance kernels, enabling faster computation.
- •Validated with simulations and application to fMRI images.
Reference
“Our estimator can be trained without computing the autocovariance kernels and it can be parallelized to provide the estimates much faster than existing approaches.”