Time Series Forecasting in Python

Marco Peixeiro

• Publisher: Manning
• Year: 2022
• ISBN:
  9781638351474
• Topic: Computer Science
• Subtopic: Data Processing
• Index: Computer Science

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model
Create univariate forecasting models that account for seasonal effects and external variables
Build multivariate forecasting models to predict many time series at once
Leverage large datasets by using deep learning for forecasting time series
Automate the forecasting process Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. About the technology
You can predict the future—with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. About the book
Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you'll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you'll soon be ready to build your own accurate, insightful forecasts. What's inside Create models for seasonal effects and external variables
Multivariate forecasting models to predict multiple time series
Deep learning for large datasets
Automate the forecasting processAbout the reader
For data scientists familiar with Python and TensorFlow. About the author
Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada's largest banks.Table of Contents
PART 1 TIME WAITS FOR NO ONE
1 Understanding time series forecasting
2 A naive prediction of the future
3 Going on a random walk
PART 2 FORECASTING WITH STATISTICAL MODELS
4 Modeling a moving average process
5 Modeling an autoregressive process
6 Modeling complex time series
7 Forecasting non-stationary time series
8 Accounting for seasonality
9 Adding external variables to our model
10 Forecasting multiple time series
11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia
PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING
12 Introducing deep learning for time series forecasting
13 Data windowing and creating baselines for deep learning
14 Baby steps with deep learning
15 Remembering the past with LSTM
16 Filtering a time series with CNN
17 Using predictions to make more predictions
18 Capstone: Forecasting the electric power consumption of a household
PART 4 AUTOMATING FORECASTING AT SCALE
19 Automating time series forecasting with Prophet
20 Capstone: Forecasting the monthly average retail price of steak in Canada
21 Going above and beyond

Table of contents

• inside front cover
• Time Series Forecasting in Python
• Copyright
• dedication
• contents
• front matter
• Part 1. Time waits for no one
• 1 Understanding time series forecasting
• 2 A naive prediction of the future
• 3 Going on a random walk
• Part 2. Forecasting with statistical models
• 4 Modeling a moving average process
• 5 Modeling an autoregressive process
• 6 Modeling complex time series
• 7 Forecasting non-stationary time series
• 8 Accounting for seasonality
• 9 Adding external variables to our model
• 10 Forecasting multiple time series
• 11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia
• Part 3. Large-scale forecasting with deep learning
• 12 Introducing deep learning for time series forecasting
• 13 Data windowing and creating baselines for deep learning
• 14 Baby steps with deep learning
• 15 Remembering the past with LSTM
• 16 Filtering a time series with CNN
• 17 Using predictions to make more predictions
• 18 Capstone: Forecasting the electric power consumption of a household
• Part 4. Automating forecasting at scale
• 19 Automating time series forecasting with Prophet
• 20 Capstone: Forecasting the monthly average retail price of steak in Canada
• 21 Going above and beyond
• Appendix. Installation instructions
• index
• inside back cover