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An Introduction to Time Series
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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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Table of Contents

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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

Time series are all around us, from stock prices and weather forecasts to economic trends and medical diagnoses. This course is ...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

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

Course Content

40 Lessons6 Quizzes1 Assessment

Introduction to Time Series

2 Lessons

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

About This Course

Course Structure

The Basics of Time Series

5 Lessons

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

5 Lessons

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

10 Lessons

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

5 Lessons

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.

Prediction Loss

Forecasting with ARIMA Models

Forecast Confidence

Quiz

Choosing, Fitting, and Evaluating Models

6 Lessons

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

Identifying and Fitting Candidate Models

Goodness of Fit and Information Criteria

Splitting Time Series Data

Diagnostic Checks

Out-of-Sample Forecasting and Evaluation

Quiz

Conclusion

1 Lessons

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

Conclusion

10.

Appendix

2 Lessons

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

Download the Temperatures Data

Credits and Links

What have you learned?

Assessment

Time Series Forecasting with Prophet in Python

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Alvaro Corrales Cano

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An Introduction to Time Series Save for later

An Introduction to Time Series
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