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...is a collective term for characteristics that the two institutions share.
Our publications are sneak peeks! ...happy learning kidz!
▀
█ Times Series Analysis for Everyone
█
█ By Bruno Goncalves
█
█ TIME TO COMPLETE:6h 2m
█ TOPICS:Time Series
▓ PUBLISHED BY:Pearson
▓ PUBLICATION DATE:September 2021
▓
▒ LECTURE DATE: 09/2021
░ LECTURE LINK: https://learning.oreilly.com | https://informit.com
░
░
:
| Sneak Peek
|
| The Sneak Peek program provides early access to Pearson video
| products and is exclusively available to Safari subscribers.
| Content for titles in this program is made available throughout
| the development cycle, so products may not be complete, edited, or
| finalized, including video post-production editing.
|
| 5+ Hours of Video Instruction
|
| Overview
|
| Times Series Analysis for Everyone LiveLessons covers the
| fundamental ideas and techniques for the analysis of time series
| data. This course introduces you to the basic concepts, ideas, and
| algorithms necessary to develop your own time series applications
| in a step-by-step and 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.
|
| About the Instructor
|
| Bruno Goncalves is a senior data scientist in the area of complex
| systems, human behavior, and finance. He has been programming in
| Python since 2005. For more than ten years, his work has focused
| on analyzing large-scale social media datasets for the temporal
| analysis of social behavior.
|
| 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 analyze in
| detail.
|
| Lesson 3: Stationarity and Trending Behavior
| Time series can exhibit characteristic types of behavior, such as
| trends, 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 require us to
| transform the original data. In this lesson we learn how to
| calculate and apply the most common transformations, how to impute
| missing data, and how to estimate basic properties of the time
| series.
|
| Lesson 5: Running Value Measures
| Perhaps the simplest time series analysis you can perform 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. In this lesson we explore how
| it 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 walké─εa sequence of positions where each step is chosen
| at randomé─εis 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 or not
| a given series is stationary.
|
| 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 reproduce most time series in a simple and
| efficient manner.
|
| 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 generalizes 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.
,