— Applied Time Series Analysis

Prerequisites & course information

This version has ended; see updated course, below. Course Description: Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). STA 137 — Applied Time Series Analysis (4 units) Course Description: Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. Prerequisite(s): STA 108 C- or better; ( MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better). Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics & Data Science majors and Statistics & Biostatistics graduate students. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). This course version is effective from, and including: Fall Quarter 2026. Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). STA 137 — Applied Time Series Analysis (4 units) Course Description: Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. Prerequisite(s): STA 108 C- or better; ( MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better). Learning Activities: Lecture 3 hour(s), Laboratory 1 hour(s). Enrollment Restriction(s): Pass One restricted to Statistics & Data Science majors and Statistics & Biostatistics graduate students. Grade Mode: Letter. General Education: Science & Engineering (SE); Quantitative Literacy (QL). This course version is effective from, and including: Fall Quarter 2026.