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
7h
Updated today
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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.
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
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Learning Roadmap
Certificate of Completion
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