We introduce new methods for the analysis of short-term time-series data, and apply it to gene expression data in yeast. These include (1) methods for automated period detection in a predominately cycling data set and (2) phase detection between phase-shifted cyclic data sets. We show how to properly correct for the problem of comparing correlation coefficents between pairs of sequences of different lengths and small alphabets. In particular, we show that the correlation coefficient of binary sequences can exhibit very counter-intuitive behavior when compared with the Hamming distance. Finally, we address the predictability of known regulators via time-series analysis, and show that less than $20\%$ of known regulatory pairs exhibit strong correlations in the Cho/Spellman data sets. By analyzing known regulatory relationships, we designed an edge detection function which identified candidate regulations with greater fidelity than standard correlation methods.
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