Topics#
Topic |
[Die24] |
[HP22] |
|---|---|---|
What is Time Series? |
1 |
1.1 |
Basic Time Series Properties |
||
Stationarity |
6.1 |
1.3.2 |
White Noise (and Random Walks) |
6.2 |
1.3.4, 2.2 |
Autocorrelation |
6.3 |
1.3.3, 2.1 |
Trend and Seasonality |
5 |
2.4, 9.1 |
Time Series Tools |
||
Differencing and Backshift Operator |
6.5.1 |
3, 3.1.2 |
Smoothing Techniques |
2.4 |
|
Unit Root Tests |
9.3 |
|
Information Criteria |
15.1 |
4.3 |
Univariate Models |
||
Autoregressive (AR) Models |
6.5, 6.7 |
3.3 |
Moving Average (MA) Models |
7.1, 7.2 |
3.2 |
ARMA/ARIMA Models |
7.2.4 |
3.4, 4 |
SARIMA Models |
5 |
|
ARCH Models |
8 |
6 |
Multivariate Models |
||
Vector Autoregression (VAR) Model |
16 |
7 |
Vector Error Correction Model (VECM) |
9.4.2, 9.4.4 |
|
State Space Models |
8.1 |
|
Kalman Filter |
8.2 |
|
Markov Switching Models |
8.4 |
|
Forecasting |
||
Basic Forecasting Concepts |
2 |
|
Point Forecasting |
10 |
|
Interval and Density Forecasting |
11 |
|
Model-Based Forecast Combination |
12 |
|
Machine Learning for Time Series |
||
Artificial Neural Networks |
10.2 |
|
Deep Learning and Backpropogation Algorithms |
10.3 |
|
Time Series Forecasting and TensorFlow |
10.4 |