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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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.

An Introduction to Time Series

Congratulations on completing "An Introduction to Time Series"! Throughout the course, you embarked on a journey to unlock the mysteries hidden within time series data. You have gained a solid understanding of essential time series concepts, explored the unique properties of time series data, and mastered the application of powerful Python tools and libraries for analysis and modeling.

In this course, you have learned about the foundational concepts of time series analysis, including stochastic processes, stationarity, and autocorrelation, and explored ARIMA models for making predictions in various domains. By understanding the properties of time series data, such as trends, seasons, cycles, and integration, you have gained insights into the unique characteristics of sequential data.


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Introduction to Time Series

The Basics of Time Series

Exploring Data

Analyze Time Series Data Using Markov Transition Fields

The Properties of Time Series

ARIMA Models

On Prediction

Choosing, Fitting, and Evaluating Models

Conclusion

Appendix

What have you learned?

Time Series Forecasting with Prophet in Python

Conclusion

Course wrap up