Time Series Midterm Review
1 Preparing for the test
2 Expected Values, Variance, etc.
3 A Brief Discussion of Stationarity
4 Autocorrelation
- 4.1 Independence
- 4.2 Serial Dependence / Autocorrelation
5 Autocorrelation Concepts and Notation
6 Practical Autocorrelation
- 6.1 Estimation!
  - 6.1.1 \(\rho_k \rightarrow 0\)
7 The Frequency Domain
8 Spectral Density
9 AR(1) Models and Filtering
10 GLPs
- 10.1 AR(1) Intro
11 Characteristic Equations for AR(1) Models
12 AR(2) models
13 AR(p)
14 MA(q) Models
15 Some examples of MA(1)
- 15.0.1 Positive theta1
- 15.0.2 Negative theta
16 MA(2)
- 16.1 Invertibility
  - 16.1.1 Criteria for invertibility
17 ARMA(p,q)
18 Blending AR and MA components
19 Psi weights with AR models:
- 19.1 Psi weights for ARMA
20 ARIMA and ARUMA
21 Forecasting
- 21.1 Forecasts from Signal-Plus-Noise
22 Forecasting setting
- 22.1 Box-Jenkins approach
23 Strategy + Notation
- 23.1 Notation
- 23.2 some math
24 Discussion of Ar1 forcasts
25 AR(p) forecast math
- 25.1 AR(p) forecasts:
  - 25.1.1 With tswge
26 Eventual Forecast Function:
27 ARMA(p,q)
- 27.1 Some math
28 Example
29 psi weights
30 Probability limits
- 30.1 An alternatve way to calculate psi weights
- 30.2 Calculating them
31 ASE
32 ARIMA forecasts
- 32.1 An example:
33 Seasonal Forecasts
- 33.1 Airline models
34 signal plus noise forecasts
- 34.1 Linear Signal
- 34.2 Cyclic signal
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Time Series Midterm Review

Unit 26 Eventual Forecast Function:

As \(\ell\) gets large, it is most reasonable to simply forecast the sample mean, because of how they converge