Time Series Analysis with Python

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

with Viktor Mehandzhiyski

4.8/5

(576)

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

7 hours of content 12664 students

Start for free

What you get:

7 hours of content
64 Interactive exercises
40 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Time Series Analysis with Python

A course by Viktor Mehandzhiyski

Start for free

What you get:

7 hours of content
64 Interactive exercises
40 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

Start for free

What you get:

7 hours of content
64 Interactive exercises
40 Downloadable resources
World-class instructor
Closed captions
Q&A support
Future course updates
Course exam
Certificate of achievement

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.

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

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

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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

Curriculum

1. Introduction

1 Lesson 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
4 min
2. Setting Up the Environment

2 Lessons 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 Read now
1 min
Installing the Necessary Packages
1 min
3. Introduction to Time Series in Python

7 Lessons 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
4 min
Notation for Time Series Data
1 min
Peculiarities of Time Series Data
3 min
Loading the Data
2 min
Examining the Data
6 min
Plotting the Data
5 min
The QQ Plot
3 min
4. Creating a Time Series Object in Python

7 Lessons 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
5 min
Using Dates as Indices
3 min
Setting the Frequency
3 min
Filling Missing Values
6 min
Adding and Removing Columns in a Data Frame
4 min
Splitting the Data
4 min
Updating the Dataset Read now
1 min
5. Working with Time Series in Python

8 Lessons 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
7 min
Random Walk
6 min
Stationarity
3 min
Determining Weak Form Stationarity
6 min
Seasonality
5 min
Correlation Between Past and Present Values
2 min
The ACF
6 min
The PACF
5 min
6. Picking the Correct Model

1 Lesson 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
3 min
7. The AR Model

12 Lessons 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
5 min
Examining the ACF and PACF of Prices
5 min
Fitting an AR(1) Model for Index Prices
5 min
Fitting Higher-Lag AR Models for Prices
9 min
Using Returns
6 min
Examining the ACF and PACF of Returns
2 min
Fitting an AR(1) Model for Returns
3 min
Fitting Higher-Lag AR Models for Returns
4 min
Normalizing Values
5 min
Model Selection for Normalized Returns (AR Model)
3 min
Examining the AR Model Residuals
6 min
Unexpected Shocks from Past Periods
1 min
8. The MA Model

7 Lessons 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
5 min
Fitting an MA(1) Model for Returns
4 min
Fitting Higher-Lag MA Models for Returns
8 min
Examining the MA Model Residuals for Returns
6 min
Model Selection for Normalized Returns (MA Model)
4 min
Fitting an MA(1) Model for Prices
5 min
Past Values and Past Errors
2 min
9. The ARMA Model

8 Lessons 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
4 min
Fitting a Simple ARMA Model for Returns
4 min
Fitting Higher-Lag ARMA Models for Returns - Part 1
5 min
Fitting Higher-Lag ARMA Models for Returns - Part 2
5 min
Fitting Higher-Lag ARMA Models for Returns - Part 3
6 min
Examining the ARMA Model Residuals of Returns
7 min
ARMA for Prices
8 min
ARMA Models and Non-Stationary Data
2 min
10. The ARIMA Model

9 Lessons 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
6 min
Fitting a Simple ARIMA Model for Prices
6 min
Fitting Higher-Lag ARIMA Models for Prices - Part 1
6 min
Fitting Higher-Lag ARIMA Models for Prices - Part 2
6 min
Higher Levels of Integration
4 min
Using ARIMA Models for Returns
3 min
Outside Factors and the ARIMAX Model
4 min
Seasonal Models - SARIMAX
8 min
Predicting Stability
2 min
11. The ARCH Model

7 Lessons 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
6 min
Volatility
3 min
A More Detailed Look of the ARCH Model
6 min
The arhc_model Method
8 min
The Simple ARCH Model
7 min
Higher-Lag ARCH Models
3 min
An ARMA Equivalent of the ARCH Model
1 min
12. The GARCH Model

5 Lessons 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
3 min
The ARMA and the GARCH
2 min
The Simple GARCH Model
3 min
Higher-Lag GARCH Models
4 min
An Alternative to the Model Selection Process
1 min
13. Auto ARIMA

6 Lessons 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
5 min
Preparing Python for Model Selection
1 min
The Default Best Fit
6 min
Basic Auto ARIMA Arguments
10 min
Advanced Auto ARIMA Arguments
5 min
The Goal Behind Modelling
1 min
14. Time Series Forecasting

8 Lessons 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
7 min
Simple Forecasting Returns with AR and MA
4 min
Intermediate (MAX Model) Forecasting
6 min
Advanced (Seasonal) Forecasting
4 min
Auto ARIMA Forecasting
5 min
Pitfalls of Forecasting
6 min
Forecasting Volatility
6 min
Appendix: Multivariate Forecasting (VARMAX)
8 min
15. Business Case

1 Lesson 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
28 min

Topics

TheoryPythonProgrammingData AnalysisData ProcessingTime SeriesForecastingMachine and Deep Learning

Tools & Technologies

Course Requirements

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.

Who Should Take This Course?

Level of difficulty: Intermediate

Aspiring data scientists
Current data scientists who are passionate about acquiring specialized knowledge

Exams and Certification

A 365 Financial Analyst Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Meet Your Instructor

Viktor Mehandzhiyski

Data Scientist at

3 Courses

3297 Reviews

70962 Students

Viktor Mehandzhiyski is a data scientist with degrees in Mathematics and Economics from Hamilton College. His expertise spans statistics, econometrics, time-series analysis, and behavioral economics, where he leverages Python, C++, MATLAB, and STATA to address complex problems in finance and economics. Passionate about teaching, he has created courses for 365 Data Science and tutored mathematics. His projects include forecasting soccer transfer prices and analyzing stock market contagion for global diversification.