Time Series Analysis in Python 2020 – Learn Python

Time Series Analysis in Python 2020 – Learn Python

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

What you’ll learn

Time Series Analysis in Python 2020 – Learn Python

  • 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.


  • You’ll need to install Anaconda. We will show you how to do that step by step.


If there is some time dependency, then you know it – the answer is time series analysis. We have created a time series course that is not only timeless but also:

Easy to understand



To the point

Packed with plenty of exercises and resources

But we know that may not be enough.

We take the most prominent tools and implement them through Python – the most popular programming language right now. With that in mind…

Welcome to Time Series Analysis in Python!

The big question in taking an online course is what to expect.

We start by exploring the fundamental time series theory to help you understand the modeling that comes afterward.

Then throughout the course, we will work with a number of Python libraries, providing you with complete training.

With these tools we will master the most widely used models out there:

AR (autoregressive model)

MA (moving-average model)

ARMA (autoregressive-moving-average model)

ARIMA (autoregressive integrated moving average model)

ARIMAX (autoregressive integrated moving average model with exogenous variables)

SARIA (seasonal autoregressive moving average model)

SARIMA (seasonal autoregressive integrated moving average model)

SARIMAX (seasonal autoregressive integrated moving average model with exogenous variables)

ARCH (autoregressive conditional heteroscedasticity model)

GARCH (generalized autoregressive conditional heteroscedasticity model)

VARMA (vector autoregressive moving average model)

We know that time series is one of those topics that always leaves some doubts.

Until now.

This course is exactly what you need to comprehend the time series once and for all.

What do you get?

Active Q&A support

Supplementary materials – notebook files, course notes, quiz questions, exercises

All the knowledge to get a job with time series analysis

A community of data science enthusiasts

A certificate of completion

Access to future updates

Solve real-life business cases that will get you the job

Who this course is for:

  • Aspiring data scientists.
  • Programming beginners.
  • People interested in quantitative finance.
  • Programmers who want to specialize in finance.
  • Finance graduates and professionals who need to better apply their knowledge in Python.

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