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Stat 131 Time Series Analysis: Introductory Examples. Instructor : Jose Blanchet (Science center 606) Instructor’s office hours (tentative): Mon 2:15 – 3:15 PM & Wed 5:45 – 6:45 PM TF: Wei Zhang Office hours: TBA.
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Instructor: Jose Blanchet (Science center 606) Instructor’s office hours (tentative): Mon 2:15 – 3:15 PM & Wed 5:45 – 6:45 PMTF: Wei ZhangOffice hours: TBA
www.courses.fas.harvard.edu/~stat131/Primary text: Brockwell, P., and Davis, R. (1991) Time Series: Theory and Methods. 2nd edition, Springer.Secondary: Brockwell, P. and Davis, R. (2002) Introduction to Time Series and Forecasting. 2nd edition. Springer.
Description: • Model building, data analysis, inference and forecasting using Auto regressive (AR), moving average (MA), ARMA, and ARIMA processes. • Stationary and non-stationary processes, seasonal processes, auto-correlation and partial auto-correlation functions, identification of models, estimation of parameters, and spectral analysis. • Prerequisite: Stat 110, recommended Stat 111 and/or Stat 139.
Grading Policy • Homework 20% (About 6 problems sets) • May discuss homework problems with other students, but must write them up independently. • Correct answers without supporting work will not receive credit. • Homework is due at the beginning of class on the due date. Late homework will not be accepted. • Your lowest homework score will be dropped when computing your final grade… (i.e. this leaves room for emergencies!) • SOME HOMEWORKS WILL REQUIRE USE OF MATLAB OR ANY OTHER COMPUTER PACKAGE…
Examples of Time Series • Monthly sales of red wine (in liters) of an Australian wine maker MONTLY from January 1980 to October 1991
Examples of Time Series • Monthly sales of red wine (in liters) of an Australian wine maker MONTLY from January 1980 to October 1991
Examples of Time Series • Monthly accidental deaths data 1973 to 1978 measured in thousands of dollars… (seasonal components)
Mean reversion of heights of individuals across generations… (Galton and Pearson)
Additional important applications: • Financial time series… • Economic variables such as inflation, exchange rates… • Engineering: linear and non-linear time series, queueing theory, signal processing…
General objective: • Given a random process identify trend, seasonality and noise (randomness) • Estimate the structure of the standardize (without trend and seasonality) series… • Analysis and forecasting…