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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.
4.7
40 Lessons
2 Projects
7h
Updated 4 months ago
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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
Learning Roadmap
1.
Introduction to Time Series
Introduction to Time Series
Get familiar with the fundamentals of univariate time series, analysis, and forecasting.
2.
The Basics of Time Series
The Basics of Time Series
Discover the logic behind time series fundamentals, from stochastic processes to statistical analysis.
3.
Exploring Data
Exploring Data
5 Lessons
5 Lessons
Examine key moments, visual tools, and tests for analyzing time series data distributions.
4.
The Properties of Time Series
The Properties of Time Series
10 Lessons
10 Lessons
Grasp the fundamentals of time series properties, including integration, autocorrelation, and decomposition.
5.
ARIMA Models
ARIMA Models
5 Lessons
5 Lessons
Map out the steps for understanding MA, AR, ARMA, ARIMA, and SARIMA models.
6.
On Prediction
On Prediction
4 Lessons
4 Lessons
Simplify complex topics for point forecasting, model accuracy, and confidence intervals in ARIMA.
7.
Choosing, Fitting, and Evaluating Models
Choosing, Fitting, and Evaluating Models
6 Lessons
6 Lessons
Master the steps to choose, fit, and evaluate time series models effectively.
9.
Appendix
Appendix
2 Lessons
2 Lessons
Discover the logic behind downloading data and valuable references for time series analysis.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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, and cycle components. You’ll then learn about linear time series models, including autoregressive (AR) processes, moving average (MA) processes, ARMA, and ARIMA. You’ll fit these models on data and forecast the future and finish with evaluating the models using various goodness of fit criteria.
By the end of this course, you’ll have a solid foundation in univariate time series analysis and the skills to explore, model, and forecast time series data using Python.
ABOUT THE AUTHOR
Alvaro Corrales Cano
Hello! My name is Álvaro, and I am a Data Scientist. I specialise in a wide array of Econometric and Machine Learning techniques, including (but not limited to) time series, causal inference and NLP.
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