inside back cover
Core concepts for time series forecasting (continued from inside front cover)
|
Core concept |
Chapter |
Section |
|---|---|---|
|
SARIMA model |
8 |
8.1 |
|
Frequency of seasonality |
8 |
8.1 |
|
Time series decomposition |
8 |
8.2 |
|
Forecasting with SARIMA |
8 |
8.3 |
|
SARIMAX model |
9 |
9.1 |
|
Caveat of SARIMAX |
9 |
9.1.2 |
|
Forecasting with SARIMAX |
9 |
9.2 |
|
Vector autoregression model (VAR) |
10 |
10.1 |
|
Granger causality test |
10 |
10.2.1 |
|
Forecasting with VAR |
10 |
10.3 |
|
Types of deep learning models |
12 |
12.2 |
|
Data windowing |
13 |
13.1 |
|
Deep neural network |
14 |
14.2 |
|
Long short-term memory (LSTM) |
15 |
15.2 |
|
Convolutional neural network (CNN) |
16 |
16.1 |
|
Autoregressive LSTM |
17 |
17.1 |
|
Working with Prophet |
19 |
19.2 |
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