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Advanced Forecasting with Python: With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR
book

Advanced Forecasting with Python: With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR

by Joos Korstanje
July 2021
Intermediate to advanced
294 pages
5h 42m
English
Apress
Content preview from Advanced Forecasting with Python: With State-of-the-Art-Models Including LSTMs, Facebook’s Prophet, and Amazon’s DeepAR
© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2021
J. KorstanjeAdvanced Forecasting with Pythonhttps://doi.org/10.1007/978-1-4842-7150-6_6

6. The ARIMA Model

Joos Korstanje1  
(1)
Maisons Alfort, France
 

Having seen several building blocks of univariate time series, in this chapter, you’re going to see a model that combines even more of the time series components: the ARIMA model.

To keep track of the different building blocks, let’s get back to the table of time series components (Table 6-1) to see where the ARIMA model is at.
Table 6-1

The Building Blocks of Univariate Time Series

Name

Explanation

Chapter

AR

Autoregression

3

MA

Moving Average

4

ARMA

Combination of AR and MA models

5

ARIMA

Adding differencing (I) to the ARMA ...

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