Have a personal or library account? Click
here
to login
Paradigm
reference-global.com
Content
Services
Paradigm
Partners
Contact
Books
Modern Time Series Forecasting with Python
Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning
Modern Time Series Forecasting with Python: Explore industry-ready time series forecasting using modern machine learning and deep learning
Chapter in the book
Modern Time Series Forecasting with Python
Publisher:
Packt Publishing Limited
By:
Manu Joseph
Paid access
|
Dec 2022
Book details
Table of contents
Table of Contents
Introducing Time Series
Acquiring and Processing Time Series Data
Analyzing and Visualizing Time Series Data
Setting a Strong Baseline Forecast
Time Series Forecasting as Regression
Feature Engineering for Time Series Forecasting
Target Transformations for Time Series Forecasting
Forecasting Time Series with Machine Learning Models
Ensembling and Stacking
Global Forecasting Models
Introduction to Deep Learning
Building Blocks of Deep Learning for Time Series
Common Modeling Patterns for Time Series
Attention and Transformers for Time Series
Strategies for Global Deep Learning Forecasting Models
Specialized Deep Learning Architectures for Forecasting
Multi-Step Forecasting
Evaluating Forecasts – Forecast Metrics
Evaluating Forecasts – Validation Strategies
PDF preview is not available for this content.
PDF ISBN:
978-1-80323-204-1
Publisher:
Packt Publishing Limited
Copyright owner:
© 2022 Packt Publishing Limited
Publication date:
2022
Language:
English
Pages:
552
Related subjects:
Computer sciences
,
Databases and data mining
Previous chapter
Modern Time Series Forecasting with Python
Next chapter