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Time Series Forecasting in Python
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
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.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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 process
About 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 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 Forecasting the electric power consumption of a household
PART 4 AUTOMATING FORECASTING AT SCALE
19 Automating time series forecasting with Prophet
20 Forecasting the monthly average retail price of steak in Canada
21 Going above and beyond
In Time Series Forecasting in Python you will learn how
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.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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 process
About 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 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 Forecasting the electric power consumption of a household
PART 4 AUTOMATING FORECASTING AT SCALE
19 Automating time series forecasting with Prophet
20 Forecasting the monthly average retail price of steak in Canada
21 Going above and beyond
- GenresNonfictionProgramming
456 pages, Paperback
Published October 4, 2022
14 people are currently reading
48 people want to read
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Displaying 1 - 8 of 8 reviews
November 1, 2022
This book covers both classical statistical forecasting model such as ARIMA, SARIMA and deep learning ones. Stationary and non-stationary models are covered. Automated forecasting with tools such as Prophet is also covered.
It provides good example and "capstone" (case studies) that are close to real use case scenarios. The code from GitHub is also good and easy to follow.
Well written overall and good starting guide. It covers some theoretical with diagrams rather than equations, but overall it is pretty light on the math side. It has a good part on residual analysis.
One topic I was expecting more coverage was exponential smoothing, which is mentioned but not fully covered.
Another thing that is a bit missing for a practical book is how to prepare when you actually have an improper time series, and you need to prepare the data. Also, no much reference to time series databases, but I guess this would go beyond just Python.
It provides good example and "capstone" (case studies) that are close to real use case scenarios. The code from GitHub is also good and easy to follow.
Well written overall and good starting guide. It covers some theoretical with diagrams rather than equations, but overall it is pretty light on the math side. It has a good part on residual analysis.
One topic I was expecting more coverage was exponential smoothing, which is mentioned but not fully covered.
Another thing that is a bit missing for a practical book is how to prepare when you actually have an improper time series, and you need to prepare the data. Also, no much reference to time series databases, but I guess this would go beyond just Python.
February 19, 2023
One of the better books on an introduction to time series forecasting using python programming.
May 3, 2023
What I liked in the book, is that it is written for not-high-end developers or for that guy at the university, who used to talk only about advanced calculus (and WarCraft 3 TFT), but it is rather a toolbook for someone who has studied Greek Linguistics and somehow has cheated the system to become a mathematician in an investment company, only because they had "heart" and really somehow "loved" maths. Ok, that's a big sentence, but I hope you got its idea.
In general, the book is well structured and lots of the main terms are explained as expected - with working examples. I have even made a whole YouTube video, presenting a chapter of the book and if I ever have free time I will make a new one.
Enjoy my video - https://youtu.be/SjhJNt8VnGw
In general, the book is well structured and lots of the main terms are explained as expected - with working examples. I have even made a whole YouTube video, presenting a chapter of the book and if I ever have free time I will make a new one.
Enjoy my video - https://youtu.be/SjhJNt8VnGw
October 21, 2022
If you want to learn about how to process time-series data with Python, this is the right book for you.
It is hands-on-oriented and has many examples (with code) to support the theory explained.
I really recommend it!
It is hands-on-oriented and has many examples (with code) to support the theory explained.
I really recommend it!
October 12, 2022
This book takes you from simple moving average to more complex deep-learning. A great way to learn how to model your time series data.
February 3, 2023
Enjoyed reading this book. It's very well-written and well-organized. Gives you great framework to analyze and model times series data on Python.
May 12, 2023
A great introduction to time series econometrics in Python
April 4, 2024
reading the first 3 chapters made me further understand the details of the topics within this book. I really enjoyed reading it.
Displaying 1 - 8 of 8 reviews