With microarray technology, gene expression profiles are produced at a rapid rate. It remains a challenge for biologists to robustly identify periodic gene expression profiles when the time series have short data length and contain a high level of noise. An effective method is proposed in this paper to analyze the periodicity of gene expression time series using singular value decomposition (SVD), singular spectrum analysis (SSA) and autoregressive (AR) model-based spectral estimation. Using these procedures, noise can be filtered out and over 85% of periodic gene expression can be identified in the mouse segmentation clock data set.