Time Series Forecasting in Python / Najlacnejšie knihy
Time Series Forecasting in Python

Code: 38609936

Time Series Forecasting in Python

by Marco Peixeiro

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting. In  Time Series Forecasting in Python  you will learn how to: Reco ... more

55.30 €

RRP: 56.43 €

You save 1.13 €


In stock at our supplier
Shipping in 14 - 21 days
Add to wishlist

You might also like

Give this book as a present today
  1. Order book and choose Gift Order.
  2. We will send you book gift voucher at once. You can give it out to anyone.
  3. Book will be send to donee, nothing more to care about.

Book gift voucher sampleRead more

More about Time Series Forecasting in Python

You get 134 loyalty points

Book synopsis

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting.

In  Time Series Forecasting in Python  you will learn how to:

  • Recognize a time series forecasting problem and build a performant predictive model
  • Create univariate forecasting models that account for seasonal effects and external variables
  • Build multivariate forecasting models to predict many time series at once
  • Leverage large datasets by using deep learning for forecasting time series
  • Automate the forecasting process

DESCRIPTION 
Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You''ll explore interesting real-world datasets like Google''s daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You''ll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code.

Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You''ll explore interesting real-world datasets like Google''s daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.

about the technology

Time series forecasting reveals hidden trends and makes predictions about the future from your data. This powerful technique has proven incredibly valuable across multiple fields—from tracking business metrics, to healthcare and the sciences. Modern Python libraries and powerful deep learning tools have opened up new methods and utilities for making practical time series forecasts.

about the book

Time Series Forecasting in Python teaches you to apply time series forecasting and get immediate, meaningful predictions. You''ll learn both traditional statistical and new deep learning models for time series forecasting, all fully illustrated with Python source code. Test your skills with hands-on projects for forecasting air travel, volume of drug prescriptions, and the earnings of Johnson & Johnson. By the time you''re done, you''ll be ready to build accurate and insightful forecasting models with tools from the Python ecosystem.

Book details

Book category Books in German Naturwissenschaften, Medizin, Informatik, Technik Informatik, EDV Informatik

55.30 €

Trending among others

All about us



Shopping guide
Digest
Books by language
Payment
Delivery 2.99 €

Collection points Bratislava a 13163 dalších

Slovensko
България Hrvatska România Magyarország Polska Česko

Copyright ©2008-26 najlacnejsie-knihy.sk All rights reservedPrivacyCookies

Account: Log in
Všetky knihy sveta na jednom mieste. Navyše za skvelé ceny.

Shopping cart ( Empty )

For free shipping
shop for 59,99 € and more

You are here: