Machine Learning for Time-Series with Python : Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
Language: English
Published by Packt Publishing, 2021
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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US$ 109.92
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nach der Bestellung gedruckt Neuware - Printed after ordering - Become proficient in deriving insights from time-series data and analyzing a model's performanceKey Features:Explore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time-series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description:Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.What You Will Learn:Understand the main classes of time-series and learn how to detect outliers and patternsChoose the right method to solve time-series problemsCharacterize seasonal and correlation patterns through autocorrelation and statistical techniquesGet to grips with time-series data visualizationUnderstand classical time-series models like ARMA and ARIMAImplement deep learning models like Gaussian processes and transformers and state-of-the-art machine learning modelsBecome familiar with many libraries like prophet, xgboost, and TensorFlowWho this book is for:This book is ideal for data analysts, data scientists, and Python developers who are looking to perform time-series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.…
Seller Inventory # 9781801819626
- Title
- Machine Learning for Time-Series with Python : Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
- Author
- Ben Auffarth
- Publisher
- Packt Publishing
- Publication year
- 2021
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1801819629
- ISBN 13
- 9781801819626
- Item weight
- 690 grams
- Dimensions
- 235x191x21 mm
Become proficient in deriving insights from time-series data and analyzing a model’s performance
Key Features
- Explore popular and modern machine learning methods including the latest online and deep learning algorithms
- Learn to increase the accuracy of your predictions by matching the right model with the right problem
- Master time-series via real-world case studies on operations management, digital marketing, finance, and healthcare
Book Description
Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.
This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.
Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.
By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.
What you will learn
- Understand the main classes of time-series and learn how to detect outliers and patterns
- Choose the right method to solve time-series problems
- Characterize seasonal and correlation patterns through autocorrelation and statistical techniques
- Get to grips with time-series data visualization
- Understand classical time-series models like ARMA and ARIMA
- Implement deep learning models, like Gaussian processes, transformers, and state-of-the-art machine learning models
- Become familiar with many libraries like Prophet, XGboost, and TensorFlow
Who this book is for
This book is ideal for data analysts, data scientists, and Python developers who are looking to perform time-series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.
Table of Contents
- Introduction to Time-Series with Python
- Time-Series Analysis with Python
- Preprocessing Time-Series
- Introduction to Machine Learning for Time Series
- Forecasting with Moving Averages and Autoregressive Models
- Unsupervised Methods for Time-Series
- Machine Learning Models for Time-Series
- Online Learning for Time-Series
- Probabilistic Models for Time-Series
- Deep Learning for Time-Series
- Reinforcement Learning for Time-Series
- Multivariate Forecasting
"Synopsis" may belong to another edition of this title.
About the Author
"About the title" may belong to another edition of this title.
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