Pearson – Times Series Analysis for Everyone LiveLessons

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Times Series Analysis for Everyone LiveLessons covers the fundamental tools and techniques for the analysis of time series data. These lessons introduce you to the basic concepts, ideas, and algorithms necessary to develop your own time series applications in a step-by-step, intuitive fashion. The lessons follow a gradual progression, from the more specific to the more abstract, taking you from the very basics to some of the most recent and sophisticated algorithms by leveraging the statsmodels, arch, and Keras state-of-the-art models.

Skill Level

Intermediate

Learn How To

Use Pandas for time series

Create visualizations of time series

Transform time series data

Apply Fourier analysis

Utilize time series correlations

Understand random walk models

Explore and fit ARIMA models

Explore and fit ARCH models

Integrate machine learning into time series analysis

Integrate deep learning into time series analysis

Who Should Take This Course

Data scientists with an interest in time series data analysis

Course Requirements

Basic algebra, calculus, and statistics and programming experience

Lesson Descriptions

Lesson 1: Pandas for Time Series. Pandas was originally developed for financial applications. As such, it was developed with time series support from day one. In this lesson we review some of the fundamental features of pandas that we use in the remainder of the course.

Lesson 2: Visualizing Time Series Modeling. Visualization is a fundamental first step when exploring and understanding a new dataset. Here we visualize and highlight important features of the example time series we will later analyze in detail.

Lesson 3: Stationarity and Trending Behavior. Time series can exhibit characteristic types of behavior such as trends and seasonal and cyclical patterns. In this lesson you learn how to identify each of these behaviors and to remove them from the time series in order to facilitate its analysis.

Lesson 4: Transforming Time Series Data. The modeling and analysis of time series often requires us to transform the original data. In this lesson you learn how to calculate and apply the most common transformations, impute missing data, and estimate basic properties of the time series.

Lesson 5: Running Value Measures. Perhaps the simplest analysis you can perform of a time series is the exploration of how various metrics evolve as a function of time. In this lesson you learn how to calculate measures using running windows

Lesson 6: Fourier Analysis. Fourier analysis is a powerful tool that enables us to not only observe the strongest frequencies present in the data but also to eliminate noise patterns and perform simple extrapolations of future values.

Lesson 7: Time Series Correlations. An important step in characterizing a time series is understanding how it correlates with itself. The auto-correlation and partial-auto-correlation functions are the two most important functions we use to determine the temporal properties of our time series.

Lesson 8: Random Walks. A random walk, a sequence of positions where each step is chosen at random, is perhaps the simplest example of time series. Here we use it as a prototypical model to understand the fundamental ideas behind time series analysis and to determine whether the series is stationary or not.

Lesson 9: ARIMA Models. The ARIMA class of models is the most popular and well-known family of time series models. It relies on the concepts of partial and (full) auto-correlation to define a simple random walk-like process that is able to simply reproduce most time series. We explore different variants of this class of models using the statsmodels Python module.

Lesson 10: ARCH Models. The ARIMA class of models requires the underlying time series to be stationary. When that assumption is not true, we need to rely instead on the ARCH class of models that generalize ARIMA to the situation, common in financial time series, in which the variance of the time series changes over time.

Lesson 11: Machine Learning with Time Series. Both ARIMA and ARCH models are classical models that were developed specifically for the modeling of time series. However, it is possible to apply a wide range of machine learning approaches to the modeling and forecasting of time varying phenomena.

Lesson 12: Overview of Deep Learning Approaches. Recurrent neural networks are a class of deep learning architectures that were developed specifically to be used in modeling sequential patterns such as sequences of words, sounds, and other related phenomena. In this lesson you learn how you can apply them directly to time series.

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