Cargando…
Time series forecasting in Python /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico Audiom |
| Idioma: | Inglés |
| Publicado: |
[Shelter Island, NY] :
Manning Publications Co.,
[2022]
|
| Edición: | [First edition]. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cim a22000007i 4500 | ||
|---|---|---|---|
| 001 | OR_on1351511770 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o h | ||
| 007 | sz zunnnnnuneu | ||
| 007 | cr nnannnuuuuu | ||
| 008 | 221122s2022 nyunnnn o i n eng d | ||
| 040 | |a ORMDA |b eng |e rda |e pn |c ORMDA |d OCLCF |d OCLCO | ||
| 024 | 8 | |a 9781617299889AU | |
| 029 | 1 | |a AU@ |b 000072997793 | |
| 035 | |a (OCoLC)1351511770 | ||
| 037 | |a 9781617299889AU |b O'Reilly Media | ||
| 050 | 4 | |a QA280 | |
| 082 | 0 | 4 | |a 519.55 |2 23/eng/20221122 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Peixeiro, Marco, |e author. | |
| 245 | 1 | 0 | |a Time series forecasting in Python / |c Marco Peixeiro. |
| 250 | |a [First edition]. | ||
| 264 | 1 | |a [Shelter Island, NY] : |b Manning Publications Co., |c [2022] | |
| 300 | |a 1 online resource (1 audio file (10 hr., 53 min.)) | ||
| 306 | |a 105300 | ||
| 336 | |a spoken word |b spw |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 344 | |a digital |2 rdatr | ||
| 347 | |a audio file |2 rdaft | ||
| 520 | |a 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 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 You can predict the future--with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. About the Book Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you'll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you'll soon be ready to build your own accurate, insightful forecasts. What's Inside Create models for seasonal effects and external variables Multivariate forecasting models to predict multiple time series Deep learning for large datasets Automate the forecasting process About the Reader For data scientists familiar with Python and TensorFlow. About the Author Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada's largest banks. Quotes The importance of time series analysis cannot be overstated. This book provides key techniques to deal with time series data in real-world applications. Indispensable. - Amaresh Rajasekharan, IBM Marco Peixeiro presents concepts clearly using interesting examples and illustrative plots. You'll be up and running quickly using the power of Python. - Ariel Andres, MD Financial Management What caught my attention were the practical examples immediately applicable to real life. He explains complex topics without the excess of mathematical formalism. - Simone Sguazza, University of Applied Sciences and Arts of Southern Switzerland. Narrated by Adam Newmark. | ||
| 588 | 0 | |a Online resource; title from title details screen (O'Reilly, viewed November 22, 2022). | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Time-series analysis |x Data processing. | |
| 650 | 0 | |a Time-series analysis |x Computer programs. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 6 | |a Série chronologique |x Informatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 650 | 7 | |a Time-series analysis |x Computer programs |2 fast | |
| 650 | 7 | |a Time-series analysis |x Data processing |2 fast | |
| 655 | 7 | |a Audiobooks |2 fast | |
| 655 | 7 | |a Audiobooks. |2 lcgft | |
| 655 | 7 | |a Livres audio. |2 rvmgf | |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781617299889AU/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 994 | |a 92 |b IZTAP | ||