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Advanced Time Series for Everyone

Published by Pearson

Beginner to intermediate content levelBeginner to intermediate

ARCH models with Python

Large-scale availability of time-varying data originating in financial, economical and social systems has led to a rise in the interest in this class of algorithms. Understanding how to model and describe time series information is a fundamental skill in any data scientist's toolkit.

In this lecture, Bruno will introduce you to the libraries that Python provides for working with time series data. We’ll quickly review the ARIMA class of models before diving in detail into the more advanced ARCH class of forecasting models. Both real and synthetic datasets will be used to illustrate the different kinds of models and their underlying assumptions.

What you’ll learn and how you can apply it

  • Use pandas to store, retrieve and manipulate time series data
  • Understand the architecture and how to use the statsmodels python package for time series analysis and fitting
  • Generate and use random walks as test beds for time series
  • Implement a wide range of ARCH models with state of the art Python based tools

This live event is for you because...

  • Need to work with large scale time varying data
  • Want to move beyond the basic ARIMA class of models
  • Want to learn how to apply advanced ARCH models
  • Apply a wide variety of time series models to real world datasets

Prerequisites

  • Basic Python
  • Pandas
  • Numpy
  • Matplotlib
  • Jupyter

Course Set-up

  • Scientific Python distribution like Anaconda
  • statsmodels package
  • ARCH package

Recommended Preparation

Recommended Follow-up

  • (live online training) Building Intelligent Analytics through Time Series Data (dates vary; search the O'Reilly Learning Platform for an upcoming date)
  • (live online training) Time Series Forecasting (dates vary; search the O'Reilly Learning Platform for an upcoming date)
  • (live online training) Hands-On Machine Learning with Python (dates vary; search the O'Reilly Learning Platform for an upcoming date)

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1 – Pandas for Time Series (50 min)

  • DataFrames and Series
  • Time Series
  • Subsetting
  • DataFrame Manipulations
  • Merge and Join
  • Timeseries transformations
  • Resampling/fill methods

Break (10 min)

Segment 2 – statsmodels for Time Series (60 min)

  • Statsmodels structure and filosophy
  • Pearson Correlation
  • Correlations of 2 timeseries
  • Autocorrelation
  • Partial Autocorrelation Function

Break (10 min)

Segment 3 – ARIMA Models (30 min)

  • Moving Average Models (MA)
  • Autoregressive Models (AR)
  • Fitting ARIMA models with statsmodels
  • Fitting Seasonal ARIMA models

Break (10 min)

Segment 4 – GARCH models (60 min)

  • Heteroscedasticy
  • Heteroscedastical Models
  • Fitting ARCH Model
  • Generalizations

Course wrap-up (10 min)

Your Instructor

  • Bruno Gonçalves

    Bruno Gonçalves is an author, public speaker, corporate trainer, and consultant specializing in Generative AI, Blockchain Analytics, and Machine Learning. He has a diverse background that spans academia and industry, having previously served as a Data Science fellow at NYU's Center for Data Science while on leave from his tenured faculty position at Aix-Marseille Université. Bruno earned his PhD in the Physics of Complex Systems in 2008. He later focused his research on applying Data Science and Machine Learning to the large-scale analysis of online human behavior.

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Skill covered

Time Series