Non-Asymptotic Analysis of Classical Spectrum Estimators for $L$-mixing Time-series Data with Unknown Means

Spectral estimation is an important tool in time series analysis, with applications including economics, astronomy, and climatology. The asymptotic theory for non-parametric estimation is well-known but the development of non-asymptotic theory is still ongoing. Our recent work obtained the first non-asymptotic error bounds on the Bartlett and Welch methods for $L$-mixing stochastic processes. The class of $L$-mixing processes contains common models in time series analysis, including autoregressive processes and measurements of geometrically ergodic Markov chains. Our prior analysis assumes that the process has zero mean. While zero-mean assumptions are common, real-world time-series data often has unknown, non-zero mean. In this work, we derive non-asymptotic error bounds for both Bartlett and Welch estimators for $L$-mixing time-series data with unknown means. The obtained error bounds are of $O(\frac{1}{\sqrt{k}})$, where $k$ is the number of data segments used in the algorithm, which are tighter than our previous results under the zero-mean assumption.