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Machine Learning for Time Series Forecasting with Python
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource
Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and time series modeling.
Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting.
Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary
Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models' performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts.
Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
Machine Learning for Time Series Forecasting with Python is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and time series modeling.
Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting.
Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary
Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models' performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts.
Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling.
224 pages, ebook
Published December 1, 2020
14 people are currently reading
33 people want to read
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Displaying 1 - 4 of 4 reviews
May 9, 2022
This book fills two important gaps that were missing in most "practical TSA" books of the main editors for years:
1) It provides enough theoretical background for you to get started with TSF on real-life time series problems, but without overwhelming you with unnecessary statistical details/proofs/theorems of the methods that underpin forecasting. While other books are too focused on math and light on real applications, this one is the opposite, so very good news for practitioners.
2) While R has been historically the "only game in town" when it comes to time series analysis (and forecasting), this book uses Python (hurray!). It provides step-by-step instructions and code samples on how to apply Python's scientific stack for time series (statsmodels, scikit-learn, keras, pandas, numpy, etc.), along with advice on best-practices, and tips and tricks that are very time-series-specific.
Apart from these two gaps, I'd say it also covers another topic that is absent in most other books on TSA: forecasting with deep learning models. It has a whole chapter on DL for forecasting (LSTMs and GRUs, mainly) using Keras, with an intro to RNN for newcomers to the DL world, and also with great tips for time-series data-prep specific to RNNs.
Remember I mentioned that this book is very industry-oriented (instead of academic-oriented like many others using R)? The final proof of that is its last chapter, "Model Deployment for Time Series Forecasting", which is dedicated to the final phase of any successful ML project: Productionalization. In this chapter, first you get to learn Azure ML using the Python SDK, and then, taking a use-case of a model developed in earlier chapters as a guiding example, you learn to operationalize TSF models in Azure ML, plus some caveats of MLOps principles specific to time series. As I said, this is a very hands-on book.
TL;DR:
Best choice for practitioners who are relatively new to TSF, who use Python, and who want to make fast (and good) forecasts with their data, with good-enough understanding of what they're coding.
1) It provides enough theoretical background for you to get started with TSF on real-life time series problems, but without overwhelming you with unnecessary statistical details/proofs/theorems of the methods that underpin forecasting. While other books are too focused on math and light on real applications, this one is the opposite, so very good news for practitioners.
2) While R has been historically the "only game in town" when it comes to time series analysis (and forecasting), this book uses Python (hurray!). It provides step-by-step instructions and code samples on how to apply Python's scientific stack for time series (statsmodels, scikit-learn, keras, pandas, numpy, etc.), along with advice on best-practices, and tips and tricks that are very time-series-specific.
Apart from these two gaps, I'd say it also covers another topic that is absent in most other books on TSA: forecasting with deep learning models. It has a whole chapter on DL for forecasting (LSTMs and GRUs, mainly) using Keras, with an intro to RNN for newcomers to the DL world, and also with great tips for time-series data-prep specific to RNNs.
Remember I mentioned that this book is very industry-oriented (instead of academic-oriented like many others using R)? The final proof of that is its last chapter, "Model Deployment for Time Series Forecasting", which is dedicated to the final phase of any successful ML project: Productionalization. In this chapter, first you get to learn Azure ML using the Python SDK, and then, taking a use-case of a model developed in earlier chapters as a guiding example, you learn to operationalize TSF models in Azure ML, plus some caveats of MLOps principles specific to time series. As I said, this is a very hands-on book.
TL;DR:
Best choice for practitioners who are relatively new to TSF, who use Python, and who want to make fast (and good) forecasts with their data, with good-enough understanding of what they're coding.
June 15, 2021
A bit basic, but great python examples
May 6, 2022
I really enjoyed reading this book. The Python examples are great, and the concepts are explained in a clear way!
to-keep-reference
February 10, 2025Lo usó De Simone para su tesis.
Displaying 1 - 4 of 4 reviews