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All LessonsFree Lessons (3)

Introduction to Time Series

About This Course Course Structure

The Basics of Time Series

Stochastic Processes

Recurrence and Stationarity

Some Basic Examples of Time Series

Exploring Data

Moments of a Time Series: Mean and Variance Moments of a Time Series: Skewness and Kurtosis Histograms and Density Plots Normality, the Q-Q Plot and the Jarque-Bera Test Quiz

Project

Analyze Time Series Data Using Markov Transition Fields

The Properties of Time Series

Integration A Note on Stocks vs. Returns The Dickey-Fuller Test The KPSS and Phillips-Perron Tests Autocorrelation Partial Autocorrelation Testing for Autocorrelation Trends, Seasons, and Cycles Filters Quiz

ARIMA Models

Moving Average Processes (MA)Autoregressive Processes (AR)ARMA and ARIMA Processes Seasonal ARIMA Quiz

On Prediction

Prediction Loss Forecasting with ARIMA Models Forecast Confidence Quiz

Choosing, Fitting, and Evaluating Models

Identifying and Fitting Candidate Models

Goodness of Fit and Information Criteria

Splitting Time Series Data

Diagnostic Checks

Out-of-Sample Forecasting and Evaluation

Conclusion

Appendix

Download the Temperatures Data Credits and Links

Course Assessment

What have you learned?

Project

Time Series Forecasting with Prophet in Python

An Introduction to Time Series/

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Quiz

Quiz

Quiz yourself on choosing, fitting, and evaluating ARIMA models.

We'll cover the following...

Technical Quiz

1.

(True or False) The autocorrelation function (ACF) and partial autocorrelation function (PACF) of a series will always tell us the order of the seasonal and non-seasonal parameters of a SARIMA model.

A.

True

B.

False

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