Lecture Overheads for Stat 519

course information (this is a summary of the contents of the Overview part of this Web site)
1st set of overheads (introduction to time series analysis): in format suitable for printing; in format presented in class (NOT suitable for printing!)
- I-1 to I-24 covered during lecture #1 (6 Jan)
- I-34 covered during lecture #2 (8 Jan)
2nd set (simple models and stationary processes): in format suitable for printing; in format presented in class
- II-1 to II-30 covered during lecture #2 (8 Jan)
- II-31 to II-65 covered during lecture #3 (10 Jan)
- II-66 to II-73 covered during lecture #4 (13 Jan)
3rd set (simple modeling of trend and seasonality): in format suitable for printing; in format presented in class
- III-1 to III-26 covered during lecture #4 (13 Jan)
- III-27 to III-77 covered during lecture #5 (15 Jan)
- III-78 to III-130 covered during lecture #6, but skipped III-101 to III-118 (17 Jan)
4th set (tests for IID/white noise): in format suitable for printing; in format presented in class
- IV-1 to IV-7 covered during lecture #6 (17 Jan)
- IV-8 to IV-32 covered during lecture #7 (22 Jan)
5th set (estimation of process mean): in format suitable for printing (same format as presented in class)
- V-1 to V-5 covered during lecture #7 (22 Jan)
- V-6 to V-14 covered during lecture #8 (24 Jan)
6th set (ACVF and ACF and their estimation): in format suitable for printing; in format presented in class
- VI-1 to VI-13 covered during lecture #8 (24 Jan)
- VI-14 to VI-26 covered during lecture #9 (27 Jan)
7th set (constructing processes from white noise): in format suitable for printing (same format as presented in class)
- VII-1 to VII-11 covered during lecture #9 (27 Jan)
- VII-12 to VII-33 covered during lecture #10 (29 Jan)
8th set (ARMA models): in format suitable for printing; format presented in class
- VIII-1 to VIII-31 covered during lecture #11 (31 Jan)
- VIII-32 to VIII-47 covered during lecture #12 (3 Feb)
9th set (calculation of ACVF for ARMA models): in format suitable for printing (same format as presented in class)
- IX-1 to IX-20 covered during lecture #12 (3 Feb)
- IX-21 to IX-51 covered during lecture #13 (5 Feb)
10th set (forecasting stationary processes): in format suitable for printing; in format presented in class
- X-1 to X-26 covered during lecture #14 (7 Feb)
- ancillary overheads (not covered in class) about uniqueness of best linear predictor (this was stated on overhead X-13)
11th set (Levinson-Durbin recursions, innovations algorithm and multi-step-ahead forecasting): in format suitable for printing (same format as presented in class)
- XI-1 to XI-19 covered during lecture #15 (10 Feb)
- XI-20 to XI-46 covered during lecture #16 (12 Feb)
12th set (PACF): in format suitable for printing (same format as presented in class)
- XII-1 to XII-3 covered during lecture #16 (12 Feb)
- XII-4 to XII-19 covered during lecture #17 (14 Feb)
13th set (ARMA modeling and parameter estimation): in format suitable for printing; in format presented in class
- XIII-1 to XIII-19 covered during lecture #17 (14 Feb)
- XIII-20 to XIII-50 covered during lecture #18 (19 Feb)
- XIII-52 to XIII-89 covered during lecture #19 (21 Feb)
- XIII-90 to XIII-120 covered during lecture #20 (24 Feb)
- XIII-121 to XIII-139 covered during lecture #21 (26 Feb)
14th set (ARIMA models): in format suitable for printing; in format presented in class
- XIV-1 to XIV-12 covered during lecture #21 (26 Feb)
- XIV-13 to XIV-45 covered during lecture #22 (28 Feb)
15th set (SARIMA models): in format suitable for printing (same format as presented in class)
- XV-1 to XV-46 covered during lecture #23 (2 Mar)
- XV-46 to XV-57 covered during lecture #24 (4 Mar)
16th set (long-range dependence): in format suitable for printing; in format presented in class
- XVI-1 to XVI-18 covered during lecture #24 (4 Mar)
17th set (regression with stationary errors): in format suitable for printing (same format as presented in class)
18th set (state-space models and Kalman filter): in format suitable for printing; in format presented in class

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