Times Series Analysis for Everyone LiveLessons (Video Training)
- By Bruno Goncalves
- Published Nov 5, 2021 by Pearson.
Online Video
- Your Price: $239.99
- List Price: $299.99
- About this video
Video accessible from your Account page after purchase.
Register your product to gain access to bonus material or receive a coupon.
Description
- Copyright 2022
- Edition: 1st
- Online Video
- ISBN-10: 0-13-694460-4
- ISBN-13: 978-0-13-694460-7
6 Hours of Video Instruction
The perfect introduction to time-based analytics
Overview
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.
About the Instructor
Bruno Gonçalves is a senior data scientist working at the intersection of Data Science and Finance. He has been programming in Python since 2005. For the past ten years, his work has focused on time series, NLP, computational linguistics applications, and social networks.
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.
About Pearson Video Training
Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.
Sample Content
Table of Contents
Introduction
Lesson 1: Pandas for Time Series
Learning objectives
1.1 DataFrames and Series
1.2 Subsetting
1.3 Time Series
1.4 DataFrame Manipulations
1.5 Pivot Tables
1.6 Merge and Join
1.7 Demo
Lesson 2: Visualizing Time Series
Learning objectives
2.1 Data Representation
2.2 Gross Domestic Product
2.3 Influenza Mortality
2.4 Sun Activity
2.5 Dow Jones Industrial Average
2.6 Airline Passengers
2.7 Demo
Lesson 3: Stationarity and Trending Behavior
Learning objectives
3.1 Non-stationarity
3.2 Trend
3.3 Demo Number 1
3.4 Seasonality
3.5 Time Series Decomposition
3.6 Demo Number 2
Lesson 4: Transforming Time Series Data
Learning objectives
4.1 Lagged Values
4.2 Differences
4.3 Data Imputation
4.4 Resampling
4.5 Jackknife Estimators
4.6 Bootstrapping
4.7 Demo
Lesson 5: Running Value Measures
Learning objectives
5.1 Windowing
5.2 Running Values
5.3 Bollinger Bands
5.4 Exponential Running Averages
5.5 Forecasting
5.6 Demo
Lesson 6: Fourier Analysis
Learning objectives
6.1 Frequency Domain
6.2 Discrete Fourier Transform
6.3 FFT for Filtering
6.4 Forecasting
6.5 Demo
Lesson 7: Time Series Correlations
Learning objectives
7.1 Pearson Correlation
7.2 Correlation of Two Time Series
7.3 Auto-Correlation
7.4 Partial Auto-Correlation
7.5 Demo
Lesson 8: Random Walks
Learning objectives
8.1 What Is a Random Walk
8.2 White Noise
8.3 Stationary versus Non-Stationary
8.4 Dicky-Fuller Test
8.5 Hurst Exponent
8.6 Demo
Lesson 9: ARIMA Models
Learning objectives
9.1 Moving Average (MA) Models
9.2 Autoregressive (AR) Model
9.3 ARIMA Model
9.4 Fitting ARIMA Models
9.5 Statsmodels for ARIMA Models9.6 Seasonal ARIMA
9.7 Demo
Lesson 10: ARCH Models
Learning objectives
10.1 Heteroscedasticity
10.2 Hertoscedastical Models
10.3 Autoregressive Conditionally Heteroscedastic (ARCH) Model
10.4 Fitting ARCH Models
10.5 Demo
Lesson 11: Machine Learning with Time Series
Learning objectives
11.1 Interpolation
11.2 Types of Machine Learning
11.3 Regression and Classification
11.4 Cross-validation
11.5 Caveats When Working with Time Series
11.6 Demo
Lesson 12: Overview of Deep Learning Approaches
Learning objectives
12.1 Feed Forward Networks (FFN)
12.2 Recurrent Neural Networks (RNN)
12.3 Gated Recurrent Units (GRU)
12.4 Long Short-term Memory (LSTM)
12.5 Demo
Summary