An Introduction to Time Series

Discover how to model, interpret, and forecast time series data. Learn about stochasticity, stationarity, ARIMA models, and decomposition. Gain skills to explore, model, and forecast using Python.

Intermediate

40 Lessons

Certificate of Completion

Discover how to model, interpret, and forecast time series data. Learn about stochasticity, stationarity, ARIMA models, and decomposition. Gain skills to explore, model, and forecast using Python.

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Explanations

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Explanations

This course includes

1 Assessment

82 Playgrounds

6 Quizzes

This course includes

1 Assessment

82 Playgrounds

6 Quizzes

Course Overview

Time series are all around us, from stock prices and weather forecasts to economic trends and medical diagnoses. This course is designed to equip you to effectively model, interpret, and forecast time series. In this course, you’ll learn time series analysis concepts, such as stochasticity, stationarity, and autocorrelation. You’ll analyze the time series by computing its various moments and by visualizing it using histogram and density plots. Next, you’ll decompose the time series into its trend, season, ...Show More

What You'll Learn

Understanding of properties of time series, including its moments, stationarity, autocorrelation, seasonality, and trend

Hands-on experience in analyzing and modeling real-world time series using Python’s statsmodel

Familiarity with modeling time series as autoregressive (AR) and moving average (MA) processes and their combinations ARMA and ARIMA

Working knowledge of point forecasting with ARIMA models using Pythons’s statsmodel

What You'll Learn

Understanding of properties of time series, including its moments, stationarity, autocorrelation, seasonality, and trend

Course Content

Introduction to Time Series

Get familiar with the fundamentals of univariate time series, analysis, and forecasting.

About This Course

Course Structure

The Basics of Time Series

Discover the logic behind time series fundamentals, from stochastic processes to statistical analysis.

Stochastic Processes Recurrence and Stationarity Some Basic Examples of Time Series Our Use Case Quiz

Exploring Data

Examine key moments, visual tools, and tests for analyzing time series data distributions.

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

Analyze Time Series Data Using Markov Transition Fields

Project

The Properties of Time Series

Grasp the fundamentals of time series properties, including integration, autocorrelation, and decomposition.

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

Map out the steps for understanding MA, AR, ARMA, ARIMA, and SARIMA models.

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

On Prediction

4 Lessons

Simplify complex topics for point forecasting, model accuracy, and confidence intervals in ARIMA.

Choosing, Fitting, and Evaluating Models

6 Lessons

Master the steps to choose, fit, and evaluate time series models effectively.

Conclusion

1 Lesson

Congratulations on mastering time series analysis with key concepts and practical tools.

Appendix

2 Lessons

Discover the logic behind downloading data and valuable references for time series analysis.

What have you learned?

Assessment

Time Series Forecasting with Prophet in Python

Project

Course Author

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