Practical Time Series Analysis 1st edition by Aileen Nielsen – Ebook PDF Instant Download/Delivery: 1492041653 , 978-1492041658
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ISBN 10: 1492041653
ISBN 13: 978-1492041658
Author: Aileen Nielsen
massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase.
Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challenges in time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.
You’ll get the guidance you need to confidently:
- Find and wrangle time series data
- Undertake exploratory time series data analysis
- Store temporal data
- Simulate time series data
- Generate and select features for a time series
- Measure error
- Forecast and classify time series with machine or deep learning
- Evaluate accuracy and performance
Practical Time Series Analysis 1st Table of contents:
Acknowledgments
1. Time Series: An Overview and a Quick History
The History of Time Series in Diverse Applications
Medicine as a Time Series Problem
Forecasting Weather
Forecasting Economic Growth
Astronomy
Time Series Analysis Takes Off
The Origins of Statistical Time Series Analysis
The Origins of Machine Learning Time Series Analysis
More Resources
2. Finding and Wrangling Time Series Data
Where to Find Time Series Data
Prepared Data Sets
Found Time Series
Retrofitting a Time Series Data Collection from a Collection of Tables
A Worked Example: Assembling a Time Series Data Collection
Constructing a Found Time Series
Timestamping Troubles
Whose Timestamp?
Guesstimating Timestamps to Make Sense of Data
What’s a Meaningful Time Scale?
Cleaning Your Data
Handling Missing Data
Upsampling and Downsampling
Smoothing Data
Seasonal Data
Time Zones
Preventing Lookahead
More Resources
3. Exploratory Data Analysis for Time Series
Familiar Methods
Plotting
Histograms
Scatter Plots
Time Series–Specific Exploratory Methods
Understanding Stationarity
Applying Window Functions
Understanding and Identifying Self-Correlation
Spurious Correlations
Some Useful Visualizations
1D Visualizations
2D Visualizations
3D Visualizations
More Resources
4. Simulating Time Series Data
What’s Special About Simulating Time Series?
Simulation Versus Forecasting
Simulations in Code
Doing the Work Yourself
Building a Simulation Universe That Runs Itself
A Physics Simulation
Final Notes on Simulations
Statistical Simulations
Deep Learning Simulations
More Resources
5. Storing Temporal Data
Defining Requirements
Live Data Versus Stored Data
Database Solutions
SQL Versus NoSQL
Popular Time Series Database and File Solutions
File Solutions
NumPy
Pandas
Standard R Equivalents
Xarray
More Resources
6. Statistical Models for Time Series
Why Not Use a Linear Regression?
Statistical Methods Developed for Time Series
Autoregressive Models
Moving Average Models
Autoregressive Integrated Moving Average Models
Vector Autoregression
Variations on Statistical Models
Advantages and Disadvantages of Statistical Methods for Time Series
More Resources
7. State Space Models for Time Series
State Space Models: Pluses and Minuses
The Kalman Filter
Overview
Code for the Kalman Filter
Hidden Markov Models
How the Model Works
How We Fit the Model
Fitting an HMM in Code
Bayesian Structural Time Series
Code for bsts
More Resources
8. Generating and Selecting Features for a Time Series
Introductory Example
General Considerations When Computing Features
The Nature of the Time Series
Domain Knowledge
External Considerations
A Catalog of Places to Find Features for Inspiration
Open Source Time Series Feature Generation Libraries
Domain-Specific Feature Examples
How to Select Features Once You Have Generated Them
Concluding Thoughts
More Resources
9. Machine Learning for Time Series
Time Series Classification
Selecting and Generating Features
Decision Tree Methods
Clustering
Generating Features from the Data
Temporally Aware Distance Metrics
Clustering Code
More Resources
10. Deep Learning for Time Series
Deep Learning Concepts
Programming a Neural Network
Data, Symbols, Operations, Layers, and Graphs
Building a Training Pipeline
Inspecting Our Data Set
Steps of a Training Pipeline
Feed Forward Networks
A Simple Example
Using an Attention Mechanism to Make Feed Forward Networks More Time-Aware
CNNs
A Simple Convolutional Model
Alternative Convolutional Models
RNNs
Continuing Our Electric Example
The Autoencoder Innovation
Combination Architectures
Summing Up
More Resources
11. Measuring Error
The Basics: How to Test Forecasts
Model-Specific Considerations for Backtesting
When Is Your Forecast Good Enough?
Estimating Uncertainty in Your Model with a Simulation
Predicting Multiple Steps Ahead
Fit Directly to the Horizon of Interest
Recursive Approach to Distant Temporal Horizons
Multitask Learning Applied to Time Series
Model Validation Gotchas
More Resources
12. Performance Considerations in Fitting and Serving Time Series Models
Working with Tools Built for More General Use Cases
Models Built for Cross-Sectional Data Don’t “Share” Data Across Samples
Models That Don’t Precompute Create Unnecessary Lag Between Measuring Data and Making a Forecast
Data Storage Formats: Pluses and Minuses
Store Your Data in a Binary Format
Preprocess Your Data in a Way That Allows You to “Slide” Over It
Modifying Your Analysis to Suit Performance Considerations
Using All Your Data Is Not Necessarily Better
Complicated Models Don’t Always Do Better Enough
A Brief Mention of Alternative High-Performance Tools
More Resources
13. Healthcare Applications
Predicting the Flu
A Case Study of Flu in One Metropolitan Area
What Is State of the Art in Flu Forecasting?
Predicting Blood Glucose Levels
Data Cleaning and Exploration
Generating Features
Fitting a Model
More Resources
14. Financial Applications
Obtaining and Exploring Financial Data
Preprocessing Financial Data for Deep Learning
Adding Quantities of Interest to Our Raw Values
Scaling Quantities of Interest Without a Lookahead
Formatting Our Data for a Neural Network
Building and Training an RNN
More Resources
15. Time Series for Government
Obtaining Governmental Data
Exploring Big Time Series Data
Upsample and Aggregate the Data as We Iterate Through It
Sort the Data
Online Statistical Analysis of Time Series Data
Remaining Questions
Further Improvements
More Resources
16. Time Series Packages
Forecasting at Scale
Google’s Industrial In-house Forecasting
Facebook’s Open Source Prophet Package
Anomaly Detection
Twitter’s Open Source AnomalyDetection Package
Other Time Series Packages
More Resources
17. Forecasts About Forecasting
Forecasting as a Service
Deep Learning Enhances Probabilistic Possibilities
Increasing Importance of Machine Learning Rather Than Statistics
Increasing Combination of Statistical and Machine Learning Methodologies
More Forecasts for Everyday Life
Index
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