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Online Course
Time Series Analysis with Python
with
Viktor Mehandzhiyski
Master time series forecasting in Python: Gain solid theoretical knowledge and work with popular time series models such as ARIMA, ARCH, and GARCH
12261 students already have enrolled
Skill level:
Intermediate
Duration:
7 hours
-
Lessons (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
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
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
Course OVERVIEW
Description
CPE Credits: 11
Field of Study: Information Technology
Delivery Method: QAS Self Study
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
- 2. Setting Up the Environment2 MinIn 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.
- 3. Introduction to Time Series in Python24 MinIn 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.
- 4. Creating a Time Series Object in Python26 MinIn 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.
- 5. Working with Time Series in Python40 MinIn 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.
- 6. Picking the Correct Model3 MinIn 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.
- 7. The AR Model54 MinIn 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 ModelExerciseExamining the ACF and PACF of PricesExerciseFitting an AR(1) Model for Index PricesExerciseFitting Higher-Lag AR Models for PricesExerciseUsing ReturnsExerciseExamining the ACF and PACF of ReturnsExerciseFitting an AR(1) Model for ReturnsExerciseFitting Higher-Lag AR Models for ReturnsExerciseNormalizing ValuesExerciseModel Selection for Normalized Returns (AR Model)ExerciseExamining the AR Model ResidualsExerciseUnexpected Shocks from Past Periods
- 8. The MA Model34 MinIn 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.
- 9. The ARMA Model41 MinIn 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 ModelExerciseFitting a Simple ARMA Model for ReturnsExerciseFitting Higher-Lag ARMA Models for Returns - Part 1Fitting Higher-Lag ARMA Models for Returns - Part 2Fitting Higher-Lag ARMA Models for Returns - Part 3ExerciseExamining the ARMA Model Residuals of ReturnsExerciseARMA for PricesExerciseARMA Models and Non-Stationary Data
- 10. The ARIMA Model45 MinIn 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 ModelExerciseFitting a Simple ARIMA Model for PricesExerciseFitting Higher-Lag ARIMA Models for Prices - Part 1Fitting Higher-Lag ARIMA Models for Prices - Part 2ExerciseHigher Levels of IntegrationExerciseUsing ARIMA Models for ReturnsExerciseOutside Factors and the ARIMAX ModelExerciseSeasonal Models - SARIMAXPredicting Stability
- 11. The ARCH Model34 MinIn 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.
- 12. The GARCH Model13 MinIn 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. Auto ARIMA28 MinIn this section of the course, we’ll automate the model selection process with the help of the AUTO ARIMA package.
- 14. Time Series Forecasting46 MinIn this section of the course, we will make forecasts about the future based on the simple models we have introduced so far.
- 15. Business Case28 MinIn 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.
- 16. Course exam15 Min
Free preview
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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