Time Series Analysis in Python

Time Series Analysis in Python: Theory, Modeling: AR to SARIMAX, Vector Models, GARCH, Auto ARIMA, Forecasting

4.60 (2749 reviews)

Udemy

platform

English

language

Data & Analytics

What you will learn

Differentiate between time series data and cross-sectional data.

Understand the fundamental assumptions of time series data and how to take advantage of them.

Transforming a data set into a time-series.

Start coding in Python and learn how to use it for statistical analysis.

Carry out time-series analysis in Python and interpreting the results, based on the data in question.

Examine the crucial differences between related series like prices and returns.

Comprehend the need to normalize data when comparing different time series.

Encounter special types of time series like White Noise and Random Walks.

Learn about "autocorrelation" and how to account for it.

Learn about accounting for "unexpected shocks" via moving averages.

Discuss model selection in time series and the role residuals play in it.

Comprehend stationarity and how to test for its existence.

Acknowledge the notion of integration and understand when, why and how to properly use it.

Realize the importance of volatility and how we can measure it.

Forecast the future based on patterns observed in the past.

Course Gallery

Time Series Analysis in Python – Screenshot 1 — Screenshot 1 – Time Series Analysis in Python

Time Series Analysis in Python – Screenshot 2 — Screenshot 2 – Time Series Analysis in Python

Time Series Analysis in Python – Screenshot 3 — Screenshot 3 – Time Series Analysis in Python

Time Series Analysis in Python – Screenshot 4 — Screenshot 4 – Time Series Analysis in Python

Loading charts...

Comidoc Review

Our Verdict

This Python-based time series analysis course offers a comprehensive exploration of various models and techniques. While explanations are generally clear, some users may struggle to keep up with the fast pace when trying to work alongside the instructor. A few inconsistencies and outdated content have been reported, making an update desirable for improved user experience. Despite these challenges, learners can expect solid foundational knowledge of time series analysis, valuable real-world examples, and short quizzes that help reinforce concepts while working independently.

What We Liked

Covers a wide range of time series models and techniques, from AR to SARIMAX and GARCH, providing a solid foundation in time series analysis
Explanations are generally clear and easy to follow, making complex concepts accessible to learners
Includes one-question quizzes that help reinforce understanding and apply learned concepts
Utilizes real-world examples, such as stock market data, to illustrate the practical applications of time series analysis

Potential Drawbacks

Some users find it challenging to work through the lectures while simultaneously using the templates due to the fast pace of the course
Occasional typos in slides can cause confusion and may require extra effort from learners to decipher
Several users have noted that the course could benefit from an update, as certain aspects are outdated or no longer supported
Lack of support or interaction from the instructor regarding questions or doubts raised by learners