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

A stochastic process has a random component, but a deterministic process doesn’t. 

Deterministic and stochastic processes are essentially different names for the same thing. 

A deterministic process has a random component, but a stochastic process doesn’t. 

Stochastic processes can be time series; deterministic processes can’t. 

What is the difference between a stochastic and a deterministic process?

What is the difference between a stochastic and a deterministic process?

The number of steps that have been calculated

Can’t reconstruct a deterministic process

**(Select all that apply.)** What do you need to know to reconstruct a deterministic process, assuming you know the logical rules it follows?

(Select all that apply.) What do you need to know to reconstruct a deterministic process, assuming you know the logical rules it follows?

Recurrence is a property of some stochastic processes whereby future observations are completely random and unpredictable. 

Recurrence is a property of white noise whereby observations follow a Gaussian distribution. 

Recurrence is a property of some stochastic processes whereby past observations can be used to predict future ones, i.e., the system has memory.

Recurrence is a property of deterministic systems whereby the process always tends to 0.

What do you understand by the term, recurrence?

What do you understand by the term, recurrence?

**(Select all that apply.)** Which options are _not_ a type of stationarity. 

(Select all that apply.) Which options are not a type of stationarity.

It is not possible to estimate the autocovariance of white noise.

What is the autocovariance ($\gamma_j$) of a white noise process?

What is the autocovariance (<math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>γ</mi><mi>j</mi></msub></mrow><annotation encoding="application/x-tex">\gamma_j</annotation></semantics></math>γj​) of a white noise process?

White noise is not important. It is, by definition, impossible to use white noise in time series modeling.

Time series models are built on the assumption that white noise cannot be part of the data-generating process. 

White noise is important because of residual analysis, provided that it follows a Gaussian distribution. 

Many time series models are built on the assumption that the data-generating process is, in part, white noise.

Why is white noise important in time series modeling?

Why is white noise important in time series modeling?

**(Fill in the blank.)** A random walk process is ________.

(Fill in the blank.) A random walk process is ________.

The basics of time series

The basics of time series

Quiz yourself on the basics of time series.

Introduction to Time Series

The Basics of Time Series

Exploring Data

The Properties of Time Series

ARIMA Models

On Prediction

Choosing, Fitting, and Evaluating Models

Conclusion

Appendix

What have you learned?

Quiz