Booktopia has been placed into Voluntary Administration. Orders have been temporarily suspended, whilst the process for the recapitalisation of Booktopia and/or sale of its business is completed, following which services may be re-established. All enquiries from creditors, including customers with outstanding gift cards and orders and placed prior to 3 July 2024, please visit https://www.mcgrathnicol.com/creditors/booktopia-group/
Add free shipping to your order with these great books
Modern Time Series Forecasting with Python : Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Manu Joseph

Modern Time Series Forecasting with Python

Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

By: Manu Joseph, Jeffrey Tackes

eBook | 26 September 2024

At a Glance

eBook


RRP $57.19

$51.99

or 4 interest-free payments of $13.00 with

Available: 26th September 2024

Preorder. Download available after release.

Learn traditional and cutting-edge Machine Learning (ML) and deep learning techniques and best practices for time series forecasting with Python, including global ML models, conformal prediction, and transformer architectures

Key Features

  • Work through examples of how to use machine learning and global machine learning models for forecasting
  • Enhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATS
  • Learn probabilistic forecasting with conformal prediction and quantile regressions
  • Purchase of the print or Kindle book includes a free eBook in PDF format

Book Description

Predicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. With Modern Time Series Forecasting with Python, Second Edition, you'll master cutting-edge deep learning architectures and advanced statistical techniques alongside classic methods like ARIMA and exponential smoothing. Learn the fundamentals from preprocessing, feature engineering, and evaluation to applying powerful machine and deep learning models, including ensemble and global methods. This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.

What you will learn

  • Build machine learning models for regression-based time series forecasting
  • Apply powerful feature engineering techniques to enhance prediction accuracy
  • Tackle common challenges like non-stationarity and seasonality
  • Combine multiple forecasts using ensembling and stacking for superior results
  • Explore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time series
  • Evaluate and validate your forecasts using best practices and statistical metrics

Who this book is for

This book is ideal for data scientists, quantitative analysts, financial analysts, meteorologists, risk analysts, and anyone interested in leveraging Python for accurate time series forecasting.

on

More in Computer Science

Amazon.com : Get Big Fast - Robert Spector

eBOOK

Probabilistic Machine Learning : Advanced Topics - Kevin P. Murphy

eBOOK