Online Course
Time Series Analysis with Python

Name: Time Series Analysis in Python Course
Price: 36 USD

Master time series forecasting in Python: Gain solid theoretical knowledge and work with popular time series models such as ARIMA, ARCH, and GARCH

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Skill level:

Intermediate

Duration:

7 hours

Lessons (7 hours)

CPE credits:

11

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Employ proven time series techniques to make reliable forecasts.
Master manual model selection principles.
Normalize data to compare and analyze different time series datasets.
Apply AR, MA, ARMA, ARIMA, ARCH, and GARCH models in Python.
Visualize time series types like white noise and random walk.

Topics & tools

TheoryPythonProgrammingData AnalysisData ProcessingTime SeriesForecastingMachine and Deep Learning

Your instructor

Viktor Mehandzhiyski

Data Analyst | Former Data Scientist at NielsenIQ | Course Author at 365 Data Science

Track record

Bringing real-world expertise from leading global companies

Academic background

Bachelor's degree, Mathematics & Economics

Ask Viktor a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 11 Field of Study: Information Technology
Delivery Method: QAS Self Study

Data Science mainly relies on working with two types of data - cross-sectional and time series. This course will help you master the latter by introducing you to the ARMA, seasonal, integrated, MAX and volatility models, as well as show you how to forecast them into the future

Prerequisites

Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
Basic familiarity with Python programming is required.
Familiarity with NumPy is helpful but not mandatory.

Advanced preparation

Curriculum

89 lessons 64 exercises 1 exam

1. Introduction

4 min

In this short section, we’ll tell you a bit more of what the course is about, how its structured and what our goal is.

4 min

In this short section, we’ll tell you a bit more of what the course is about, how its structured and what our goal is.

What does the Course Cover Free
2. Setting Up the Environment

2 min

In this part of the course, we will explain to you how to set up Python 3 and then load up Jupyter. We’ll also show you what the Anaconda Prompt is and how we use it to download and import new modules.

2 min

In this part of the course, we will explain to you how to set up Python 3 and then load up Jupyter. We’ll also show you what the Anaconda Prompt is and how we use it to download and import new modules.

Setting up the Environment Free

Installing the Necessary Packages Free
3. Introduction to Time Series in Python

24 min

In this section of the course, we are going to learn what makes a dataset a time series, and discuss what separates it from cross-sectional data. We’ll introduce the appropriate mathematical notation for such data before loading up a dataset and quickly examining it.

24 min

In this section of the course, we are going to learn what makes a dataset a time series, and discuss what separates it from cross-sectional data. We’ll introduce the appropriate mathematical notation for such data before loading up a dataset and quickly examining it.

Introduction to Time Series Data Free

Exercise Free

Notation for Time Series Data Free

Exercise Free

Peculiarities of Time Series Data Free

Exercise Free

Loading the Data Free

Exercise Free

Examining the Data Free

Exercise Free

Plotting the Data Free

Exercise Free

The QQ Plot Free

Exercise Free
4. Creating a Time Series Object in Python

26 min

In this section of the course, we will go through the pre-processing aspects of working with time series. We’ll see how to interpret string text as dates and set these dates as indices of the data set. We’ll then set a fixed frequency and account for any missing values before splitting up the set for training and testing. In the appendix, we’ll show you how to import data directly from Yahoo Finance, so you can conduct your own analysis after completing the course.

26 min

In this section of the course, we will go through the pre-processing aspects of working with time series. We’ll see how to interpret string text as dates and set these dates as indices of the data set. We’ll then set a fixed frequency and account for any missing values before splitting up the set for training and testing. In the appendix, we’ll show you how to import data directly from Yahoo Finance, so you can conduct your own analysis after completing the course.

Transforming String Inputs into Date Time Values

Exercise

Using Dates as Indices

Exercise

Setting the Frequency

Exercise

Filling Missing Values

Exercise

Adding and Removing Columns in a Data Frame

Exercise

Splitting the Data

Exercise

Updating the Dataset
5. Working with Time Series in Python

40 min

In this section of the course, we’ll examine and visualize some important types of time series, like white noise and a random walk. We’ll then discuss important concepts like stationarity, seasonality and autocorrelation, before exploring the ACF and PACF of a S&P 500’s prices.

40 min

In this section of the course, we’ll examine and visualize some important types of time series, like white noise and a random walk. We’ll then discuss important concepts like stationarity, seasonality and autocorrelation, before exploring the ACF and PACF of a S&P 500’s prices.

White Noise

Exercise

Random Walk

Exercise

Stationarity

Exercise

Determining Weak Form Stationarity

Exercise

Seasonality

Exercise

Correlation Between Past and Present Values

Exercise

The ACF

Exercise

The PACF

Exercise
6. Picking the Correct Model

3 min

In this short section, we’ll discuss the general rules of manual model selection. We will talk about which models we prefer, what we want to avoid and how to decide between models. We’ll talk about the Log-likelihood and information criterion as measurements of preference among similar models.

3 min

In this short section, we’ll discuss the general rules of manual model selection. We will talk about which models we prefer, what we want to avoid and how to decide between models. We’ll talk about the Log-likelihood and information criterion as measurements of preference among similar models.

Picking the Correct Model

Exercise
7. The AR Model

54 min

In this section, we’ll introduce the Autoregressive Model and see how well it models market index prices and returns. We’ll discuss how to use the PACF to determine the appropriate number of lags for the model and explore the concept of normalizing values and its impact on model selection.

54 min

In this section, we’ll introduce the Autoregressive Model and see how well it models market index prices and returns. We’ll discuss how to use the PACF to determine the appropriate number of lags for the model and explore the concept of normalizing values and its impact on model selection.

The AR Model

Exercise

Examining the ACF and PACF of Prices

Exercise

Fitting an AR(1) Model for Index Prices

Exercise

Fitting Higher-Lag AR Models for Prices

Exercise

Using Returns

Exercise

Examining the ACF and PACF of Returns

Exercise

Fitting an AR(1) Model for Returns

Exercise

Fitting Higher-Lag AR Models for Returns

Exercise

Normalizing Values

Exercise

Model Selection for Normalized Returns (AR Model)

Exercise

Examining the AR Model Residuals

Exercise

Unexpected Shocks from Past Periods
8. The MA Model

34 min

In this section, we’ll introduce the Moving Average model and see how well it describes price returns. We’ll also have a look at how the MA model performs when dealing with non-stationary data and comment on the mathematical arguments for and against using such models for index prices.

34 min

In this section, we’ll introduce the Moving Average model and see how well it describes price returns. We’ll also have a look at how the MA model performs when dealing with non-stationary data and comment on the mathematical arguments for and against using such models for index prices.

The MA Model

Exercise

Fitting an MA(1) Model for Returns

Exercise

Fitting Higher-Lag MA Models for Returns

Exercise

Examining the MA Model Residuals for Returns

Exercise

Model Selection for Normalized Returns (MA Model)

Exercise

Fitting an MA(1) Model for Prices

Exercise

Past Values and Past Errors
9. The ARMA Model

41 min

In this section, we’ll combine the two models we just examined – the AR and MA – into one: the ARMA. We’ll examine how they synergize and limit the drawbacks each model has on its own. We’ll then talk about the issues that come along with finding the best-fitting ARMA model and see how checking the model residuals can be beneficial in model selection.

41 min

In this section, we’ll combine the two models we just examined – the AR and MA – into one: the ARMA. We’ll examine how they synergize and limit the drawbacks each model has on its own. We’ll then talk about the issues that come along with finding the best-fitting ARMA model and see how checking the model residuals can be beneficial in model selection.

The ARMA Model

Exercise

Fitting a Simple ARMA Model for Returns

Exercise

Fitting Higher-Lag ARMA Models for Returns - Part 1

Fitting Higher-Lag ARMA Models for Returns - Part 2

Fitting Higher-Lag ARMA Models for Returns - Part 3

Exercise

Examining the ARMA Model Residuals of Returns

Exercise

ARMA for Prices

Exercise

ARMA Models and Non-Stationary Data
10. The ARIMA Model

45 min

In this section of the course, we’ll talk about “integration” and integrated models. We’ll explain why and when we use them, as well as when we should avoid them. Here, we’ll also briefly explain the idea of “MAX” models and how to add exogenous variables to any time series model.

45 min

In this section of the course, we’ll talk about “integration” and integrated models. We’ll explain why and when we use them, as well as when we should avoid them. Here, we’ll also briefly explain the idea of “MAX” models and how to add exogenous variables to any time series model.

The ARIMA Model

Exercise

Fitting a Simple ARIMA Model for Prices

Exercise

Fitting Higher-Lag ARIMA Models for Prices - Part 1

Fitting Higher-Lag ARIMA Models for Prices - Part 2

Exercise

Higher Levels of Integration

Exercise

Using ARIMA Models for Returns

Exercise

Outside Factors and the ARIMAX Model

Exercise

Seasonal Models - SARIMAX

Predicting Stability
11. The ARCH Model

34 min

In this section, we’ll talk about the idea of measuring volatility when we’re looking for stability in our investments. We’ll explain the multiple layers of ARCH models and how they differ from the ARMA family of models we just examined. We’ll spend some time discussing the vast functionality of the “arch_model” method and why it’s important to know the default values for many of its arguments.

34 min

In this section, we’ll talk about the idea of measuring volatility when we’re looking for stability in our investments. We’ll explain the multiple layers of ARCH models and how they differ from the ARMA family of models we just examined. We’ll spend some time discussing the vast functionality of the “arch_model” method and why it’s important to know the default values for many of its arguments.

The ARCH Model

Exercise

Volatility

Exercise

A More Detailed Look of the ARCH Model

Exercise

The arhc_model Method

Exercise

The Simple ARCH Model

Higher-Lag ARCH Models

Exercise

An ARMA Equivalent of the ARCH Model
12. The GARCH Model

13 min

In this section of the course, we’ll discuss the generalized version of the ARCH model, also known as the GARCH. We’ll explore why this model is more widely used, how it outperforms high-order ARCH models and why it looks so similar to the ARMA. We’ll then empirically test the known fact that the GARCH(1,1) is the best model for measuring the volatility of price returns.

13 min

In this section of the course, we’ll discuss the generalized version of the ARCH model, also known as the GARCH. We’ll explore why this model is more widely used, how it outperforms high-order ARCH models and why it looks so similar to the ARMA. We’ll then empirically test the known fact that the GARCH(1,1) is the best model for measuring the volatility of price returns.

The GARCH Model

Exercise

The ARMA and the GARCH

Exercise

The Simple GARCH Model

Exercise

Higher-Lag GARCH Models

Exercise

An Alternative to the Model Selection Process
13. Auto ARIMA

28 min

In this section of the course, we’ll automate the model selection process with the help of the AUTO ARIMA package.

28 min

In this section of the course, we’ll automate the model selection process with the help of the AUTO ARIMA package.

Auto ARIMA

Preparing Python for Model Selection

The Default Best Fit

Basic Auto ARIMA Arguments

Advanced Auto ARIMA Arguments

The Goal Behind Modelling
14. Time Series Forecasting

46 min

In this section of the course, we will make forecasts about the future based on the simple models we have introduced so far.

46 min

In this section of the course, we will make forecasts about the future based on the simple models we have introduced so far.

Introduction to Time Series Forecasting

Simple Forecasting Returns with AR and MA

Intermediate (MAX Model) Forecasting

Advanced (Seasonal) Forecasting

Auto ARIMA Forecasting

Pitfalls of Forecasting

Forecasting Volatility

Appendix: Multivariate Forecasting (VARMAX)
15. Business Case

28 min

In this final part of the course, we will examine how a real-life event like the Dieselgate scandal can alter the trends in time series data.

28 min

In this final part of the course, we will examine how a real-life event like the Dieselgate scandal can alter the trends in time series data.

Business Case - A Look Into the Automobile Industry
16. Course exam

15 min

15 min

Course exam

Free lessons

1.1 What does the Course Cover

4 min

2.1 Setting up the Environment

1 min

2.2 Installing the Necessary Packages

1 min

3.1 Introduction to Time Series Data

4 min

3.2 Notation for Time Series Data

1 min

3.3 Peculiarities of Time Series Data

3 min

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