5. Time Series
by Sebastian Raschka, Robert Layton, Martin Czygan, Phuong Vo.T.H, Fabrizio Romano,
Python: Real-World Data Science
- Data Science with Python
- Table of Contents
- Data Science with Python
- Meet Your Course Guide
- What's so cool about Data Science?
- Course Structure
- Course Journey
- The Course Roadmap and Timeline
- 1. Course Module 1: Python Fundamentals
- 1. Introduction and First Steps – Take a Deep Breath
- A proper introduction
- Enter the Python
- About Python
- What are the drawbacks?
- Who is using Python today?
- Setting up the environment
- What you need for this course
- How you can run a Python program
- How is Python code organized
- Python's execution model
- Guidelines on how to write good code
- The Python culture
- A note on the IDEs
- 2. Object-oriented Design
- 3. Objects in Python
- 4. When Objects Are Alike
- 5. Expecting the Unexpected
- 6. When to Use Object-oriented Programming
- 7. Python Data Structures
- 8. Python Object-oriented Shortcuts
- 9. Strings and Serialization
- 10. The Iterator Pattern
- 11. Python Design Patterns I
- 12. Python Design Patterns II
- 13. Testing Object-oriented Programs
- 14. Concurrency
- 1. Introduction and First Steps – Take a Deep Breath
- 2. Course Module 2: Data Analysis
- 1. Introducing Data Analysis and Libraries
- 2. NumPy Arrays and Vectorized Computation
- 3. Data Analysis with pandas
- 4. Data Visualization
- 5. Time Series
- 6. Interacting with Databases
- 7. Data Analysis Application Examples
- 3. Course Module 3: Data Mining
- 1. Getting Started with Data Mining
- 2. Classifying with scikit-learn Estimators
- 3. Predicting Sports Winners with Decision Trees
- 4. Recommending Movies Using Affinity Analysis
- 5. Extracting Features with Transformers
- 6. Social Media Insight Using Naive Bayes
- 7. Discovering Accounts to Follow Using Graph Mining
- 8. Beating CAPTCHAs with Neural Networks
- 9. Authorship Attribution
- 10. Clustering News Articles
- 11. Classifying Objects in Images Using Deep Learning
- 12. Working with Big Data
- 13. Next Steps…
- Chapter 1 – Getting Started with Data Mining
- Chapter 2 – Classifying with scikit-learn Estimators
- Chapter 3: Predicting Sports Winners with Decision Trees
- Chapter 4 – Recommending Movies Using Affinity Analysis
- Chapter 5 – Extracting Features with Transformers
- Chapter 6 – Social Media Insight Using Naive Bayes
- Chapter 7 – Discovering Accounts to Follow Using Graph Mining
- Chapter 8 – Beating CAPTCHAs with Neural Networks
- Chapter 9 – Authorship Attribution
- Chapter 10 – Clustering News Articles
- Chapter 11 – Classifying Objects in Images Using Deep Learning
- Chapter 12 – Working with Big Data
- More resources
- 4. Course Module 4: Machine Learning
- 1. Giving Computers the Ability to Learn from Data
- How to transform data into knowledge
- The three different types of machine learning
- An introduction to the basic terminology and notations
- A roadmap for building machine learning systems
- Using Python for machine learning
- 2. Training Machine Learning Algorithms for Classification
- 3. A Tour of Machine Learning Classifiers Using scikit-learn
- 4. Building Good Training Sets – Data Preprocessing
- 5. Compressing Data via Dimensionality Reduction
- 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- 7. Combining Different Models for Ensemble Learning
- 8. Predicting Continuous Target Variables with Regression Analysis
- Introducing a simple linear regression model
- Exploring the Housing Dataset
- Implementing an ordinary least squares linear regression model
- Fitting a robust regression model using RANSAC
- Evaluating the performance of linear regression models
- Using regularized methods for regression
- Turning a linear regression model into a curve – polynomial regression
- A. Reflect and Test Yourself! Answers
- Module 2: Data Analysis
- Module 3: Data Mining
- Chapter 1: Getting Started with Data Mining
- Chapter 2: Classifying with scikit-learn Estimators
- Chapter 3: Predicting Sports Winners with Decision Trees
- Chapter 4: Recommending Movies Using Affinity Analysis
- Chapter 5: Extracting Features with Transformers
- Chapter 6: Social Media Insight Using Naive Bayes
- Chapter 7: Discovering Accounts to Follow Using Graph Mining
- Chapter 8: Beating CAPTCHAs with Neural Networks
- Chapter 9: Authorship Attribution
- Chapter 10: Clustering News Articles
- Chapter 11: Classifying Objects in Images Using Deep Learning
- Chapter 12: Working with Big Data
- Module 4: Machine Learning
- Chapter 1: Giving Computers the Ability to Learn from Data
- Chapter 2: Training Machine Learning
- Chapter 3: A Tour of Machine Learning Classifiers Using scikit-learn
- Chapter 4: Building Good Training Sets – Data Preprocessing
- Chapter 5: Compressing Data via Dimensionality Reduction
- Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Tuning
- Chapter 7: Combining Different Models for Ensemble Learning
- Chapter 8: Predicting Continuous Target Variables with Regression Analysis
- B. Bibliography
- 1. Giving Computers the Ability to Learn from Data
- Index
Time series typically consist of a sequence of data points coming from measurements taken over time. This kind of data is very common and occurs in a multitude of fields.
A business executive is interested in stock prices, prices of goods and services or monthly sales figures. A meteorologist takes temperature measurements several times a day and also keeps records of precipitation, humidity, wind direction and force. A neurologist can use electroencephalography to measure electrical activity of the brain along the scalp. A sociologist can use campaign contribution data to learn about political parties and their supporters and use these insights as an argumentation aid. More examples for time series data can be enumerated almost endlessly.
In general, time series serve two purposes. First, they help us to learn about the underlying process that generated the data. On the other hand, we would like to be able to forecast future values of the same or related series using existing data. When we measure temperature, precipitation or wind, we would like to learn more about more complex things, such as weather or the climate of a region and how various factors interact. At the same time, we might be interested in weather forecasting.
In this chapter we will explore the time series capabilities of pandas. Apart from its powerful core data structures – the series and the DataFrame – pandas comes with helper functions for dealing with time related data. With its extensive built-in optimizations, pandas is capable of handling large time series with millions of data points with ease.
We will gradually approach time series, starting with the basic building blocks of date and time objects.
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