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Forecasting Time Series Data with Fac Prophet: Build, improve, and optimize t...
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Item specifics
- Condition
- Book Title
- Forecasting Time Series Data with Facebook Prophet: Build, improv
- ISBN
- 9781800568532
- Publication Year
- 2021
- Type
- Textbook
- Format
- Trade Paperback
- Language
- English
- Subject Area
- Computers, Technology & Engineering
- Publication Name
- Forecasting Time Series Data with Facebook Prophet : Build, Improve, and Optimize Time Series Forecasting Models Using the Advanced Forecasting Tool
- Publisher
- Packt Publishing, The Limited
- Subject
- Engineering (General), Machine Theory, Neural Networks, Data Processing
- Number of Pages
- 270 Pages
About this product
Product Identifiers
Publisher
Packt Publishing, The Limited
ISBN-10
1800568533
ISBN-13
9781800568532
eBay Product ID (ePID)
11050393299
Product Key Features
Publication Year
2021
Subject
Engineering (General), Machine Theory, Neural Networks, Data Processing
Number of Pages
270 Pages
Language
English
Publication Name
Forecasting Time Series Data with Facebook Prophet : Build, Improve, and Optimize Time Series Forecasting Models Using the Advanced Forecasting Tool
Type
Textbook
Subject Area
Computers, Technology & Engineering
Format
Trade Paperback
Additional Product Features
Dewey Edition
23
Dewey Decimal
006.754
Synopsis
Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using PythonKey Features* Learn how to use the open-source forecasting tool Facebook Prophet to improve your forecasts* Build a forecast and run diagnostics to understand forecast quality* Fine-tune models to achieve high performance, and report that performance with concrete statisticsBook DescriptionProphet enables Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet's cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code.You will begin by exploring the evolution of time series forecasting, from the basic early models to the advanced models of the present day. The book will demonstrate how to install and set up Prophet on your machine and build your fi rst model with only a few lines of code. You'll then cover advanced features such as visualizing your forecasts, adding holidays, seasonality, and trend changepoints, handling outliers, and more, along with understanding why and how to modify each of the default parameters. Later chapters will show you how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models and see some useful features when running Prophet in production environments.By the end of this Prophet book, you will be able to take a raw time series dataset and build advanced and accurate forecast models with concise, understandable, and repeatable code.What you will learn* Gain an understanding of time series forecasting, including its history, development, and uses* Understand how to install Prophet and its dependencies* Build practical forecasting models from real datasets using Python* Understand the Fourier series and learn how it models seasonality* Decide when to use additive and when to use multiplicative seasonality* Discover how to identify and deal with outliers in time series data* Run diagnostics to evaluate and compare the performance of your modelsWho this book is forThis book is for data scientists, data analysts, machine learning engineers, software engineers, project managers, and business managers who want to build time series forecasts in Python. Working knowledge of Python and a basic understanding of forecasting principles and practices will be useful to apply the concepts covered in this book more easily., Create and improve high-quality automated forecasts for time series data that have strong seasonal effects, holidays, and additional regressors using Python Key Features Learn how to use the open-source forecasting tool Facebook Prophet to improve your forecasts Build a forecast and run diagnostics to understand forecast quality Fine-tune models to achieve high performance, and report that performance with concrete statistics Book Description Prophet enables Python and R developers to build scalable time series forecasts. This book will help you to implement Prophet's cutting-edge forecasting techniques to model future data with higher accuracy and with very few lines of code. You will begin by exploring the evolution of time series forecasting, from the basic early models to the advanced models of the present day. The book will demonstrate how to install and set up Prophet on your machine and build your fi rst model with only a few lines of code. You'll then cover advanced features such as visualizing your forecasts, adding holidays, seasonality, and trend changepoints, handling outliers, and more, along with understanding why and how to modify each of the default parameters. Later chapters will show you how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models and see some useful features when running Prophet in production environments. By the end of this Prophet book, you will be able to take a raw time series dataset and build advanced and accurate forecast models with concise, understandable, and repeatable code. What You Will Learn Gain an understanding of time series forecasting, including its history, development, and uses Understand how to install Prophet and its dependencies Build practical forecasting models from real datasets using Python Understand the Fourier series and learn how it models seasonality Decide when to use additive and when to use multiplicative seasonality Discover how to identify and deal with outliers in time series data Run diagnostics to evaluate and compare the performance of your models Who this Book is for This book is for data scientists, data analysts, machine learning engineers, software engineers, project managers, and business managers who want to build time series forecasts in Python. Working knowledge of Python and a basic understanding of forecasting principles and practices will be useful to apply the concepts covered in this book more easily.
LC Classification Number
TK5105.88817
Item description from the seller
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- 8***5 (19)- Feedback left by buyer.Past 6 monthsVerified purchaseI didn't see a verbal description of this book as "hardcover" or paperback. Cover design and price were similar to both cover styles. I thought I'd left a question for the seller, but I may have done something wrong. The package was too small to match the hardcovers previously received so I returned it unopened. Seller was very efficient, and packaging offered excellent protection. Haven't checked to see if refund was processed.Pogo 3 : Evidence to the Contrary: The Complete Syndicated Comic Strips, Hard... (#354953679255)
- r***2 (257)- Feedback left by buyer.Past monthVerified purchaseTarot pictured not the one received (I was sent a cheaper brand of the wrong design). Automated unrelated reply about delivery dates (?!) when I asked what happened. No response to further contact. To add further insult to injury the cards were sent from USA to UK in a paper envelope with no protection so were mangled on arrival. You can't get this deck in the UK, hence my order, so postage was expensive. So wrong deck, overpriced, late, destroyed, & terrible-to-no customer service! Yay! Avoid!!
- w***t (669)- Feedback left by buyer.Past 6 monthsVerified purchasePERFECT TRANSACTION! Shipped right after payment, well packaged, arrived during the estimated time. The item is in great condition EXACTLY as described. Very Happy, very nice purchase. Excellent communication. Thank youWindfall Battleships : Agincourt, Canada, Erin, Eagle and the Balkan and Lati... (#386149502698)