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Getting started with time series forecasting in Python

Introductory lesson on time series forecasting in Python. Marco Peixeiro explores the random walk model, MA(q) and AR(p) models. Throughout the lesson, he explores the foundational concept of stationarity, and learn how to use the ACF and PACF plots for forecasting

Bibliographic Details
Format: eBook
Language:English
Published: [Place of publication not identified] Manning Publications 2022
Edition:[First edition]
Subjects:
Time-series Analysis / Computer Programs / Fast / (ocolc)fst01151191
Python (computer Program Language) / Http://id.loc.gov/authorities/subjects/sh96008834
Time-series Analysis / Data Processing
Time-series Analysis / Data Processing / Fast / (ocolc)fst01151192
Time-series Analysis / Computer Programs / Http://id.loc.gov/authorities/subjects/sh85135431
Série Chronologique / Informatique
Python (langage De Programmation)
Python (computer Program Language) / Fast / (ocolc)fst01084736
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Description
Summary:Introductory lesson on time series forecasting in Python. Marco Peixeiro explores the random walk model, MA(q) and AR(p) models. Throughout the lesson, he explores the foundational concept of stationarity, and learn how to use the ACF and PACF plots for forecasting
Physical Description:1 video file (34 min.) sound, color

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