STA 356 Time Series/Forecasting
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STA 356 TIME SERIES/FORECASTING. Credits: 0.00, Hours: 3.0.
TTh 12:40-1:55, 025 Old Chem
We will cover some basic exploratory data analysis (autocorrelations,
periodogram etc.), autoregressive/moving average models, and inference
in dynamic state space models. Dynamic state space models are time
series models based on assuming that there are some underlying
parameters (like mean level mu[t] of y[t]; rate of change d[t]; maybe
seasonal components etc) which describe underlying structure and which
"evolve" between time periods. We will discuss Bayesian inference in
such models.
Discussion of dynamic state space models
will include basic concepts and posterior inference for standard normal
dynamic linear models (NDLM), posterior simulation based on
forward filtering backward smoothing, extensions to conditionally NDLM
models and general non-normal, non-linear models.
Students are encouraged to bring applications arising from their own
research to class.
Class Format:
Lectures on Tuesday and Thursday.
Grading:
Based on homework and two midterms.
Textbooks:
West, M. and Harrison, P.J. (1997), Bayesian Forecasting and Dynamic
Models, Springer-Verlag,
(2nd Edition).
The book covers Bayesian inference in dynamic state space models.
Diggle, P., Time Series: A Biostatistical Introduction,
Oxford University Press, ISBN 0-19-852226-6 (pbk).
This book will serve as reference for basic concepts of time series analysis,
i.e., various exploratory data analysis, basic ARMA modelling etc.
Any alternative introductory time series text is fine.
Prerequisites
STA 214, 215, 242 and/or 244, or equivalent. Some prior
computing experience is useful.
Reading List
Some suggested reading.
pm@stat.duke.edu
since September 1st.