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Machine Learning for Time Series Forecasting with Python
This book is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. --
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Newark :
John Wiley & Sons, Incorporated,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 008 | 201219s2020 xx o ||| 0 eng d | ||
| 040 | |a EBLCP |b eng |c EBLCP |d EBLCP |d LIV |d REDDC |d OCLCF |d OCLCQ |d OCLCO |d OCLCL | ||
| 020 | |a 9781119682370 | ||
| 020 | |a 1119682371 | ||
| 029 | 1 | |a AU@ |b 000068419117 | |
| 035 | |a (OCoLC)1227389845 | ||
| 050 | 1 | 4 | |a QA76.73.P98 |b .L39 2020 |
| 082 | 0 | 4 | |a 006.31 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Lazzeri, Francesca. | |
| 245 | 1 | 0 | |a Machine Learning for Time Series Forecasting with Python |h [electronic resource]. |
| 260 | |a Newark : |b John Wiley & Sons, Incorporated, |c 2020. | ||
| 300 | |a 1 online resource (227 p.) | ||
| 500 | |a Description based upon print version of record. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright -- About the Author -- About the Technical Editor -- Acknowledgments -- Contents at a Glance -- Contents -- Introduction -- Chapter 1 Overview of Time Series Forecasting -- Flavors of Machine Learning for Time Series Forecasting -- Supervised Learning for Time Series Forecasting -- Python for Time Series Forecasting -- Experimental Setup for Time Series Forecasting -- Conclusion -- Chapter 2 How to Design an End-to-End Time Series Forecasting Solution on the Cloud -- Time Series Forecasting Template -- Business Understanding and Performance Metrics | |
| 505 | 8 | |a Data Ingestion -- Data Exploration and Understanding -- Data Pre-processing and Feature Engineering -- Modeling Building and Selection -- An Overview of Demand Forecasting Modeling Techniques -- Model Evaluation -- Model Deployment -- Forecasting Solution Acceptance -- Use Case: Demand Forecasting -- Conclusion -- Chapter 3 Time Series Data Preparation -- Python for Time Series Data -- Common Data Preparation Operations for Time Series -- Time stamps vs. Periods -- Converting to Time stamps -- Providing a Format Argument -- Indexing -- Time/Date Components -- Frequency Conversion | |
| 505 | 8 | |a Time Series Exploration and Understanding -- How to Get Started with Time Series Data Analysis -- Data Cleaning of Missing Values in the Time Series -- Time Series Data Normalization and Standardization -- Time Series Feature Engineering -- Date Time Features -- Lag Features and Window Features -- Rolling Window Statistics -- Expanding Window Statistics -- Conclusion -- Chapter 4 Introduction to Autoregressive and Automated Methods for Time Series Forecasting -- Autoregression -- Moving Average -- Autoregressive Moving Average -- Autoregressive Integrated Moving Average | |
| 505 | 8 | |a Automated Machine Learning -- Conclusion -- Chapter 5 Introduction to Neural Networks for Time Series Forecasting -- Reasons to Add Deep Learning to Your Time Series Toolkit -- Deep Learning Neural Networks Are Capable of Automatically Learning and Extracting Features from Raw and Imperfect Data -- Deep Learning Supports Multiple Inputs and Outputs -- Recurrent Neural Networks Are Good at Extracting Patterns from Input Data -- Recurrent Neural Networks for Time Series Forecasting -- Recurrent Neural Networks -- Long Short-Term Memory -- Gated Recurrent Unit | |
| 505 | 8 | |a How to Prepare Time Series Data for LSTMs and GRUs -- How to Develop GRUs and LSTMs for Time Series Forecasting -- Keras -- TensorFlow -- Univariate Models -- Multivariate Models -- Conclusion -- Chapter 6 Model Deployment for Time Series Forecasting -- Experimental Set Up and Introduction to Azure Machine Learning SDK for Python -- Workspace -- Experiment -- Run -- Model -- Compute Target, RunConfiguration, and ScriptRunConfig -- Image and Webservice -- Machine Learning Model Deployment -- How to Select the Right Tools to Succeed with Model Deployment | |
| 520 | |a This book is an incisive and straightforward examination of one of the most crucial elements of decision-making in finance, marketing, education, and healthcare: time series modeling. -- |c Edited summary from book. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Machine learning |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 758 | |i has work: |a Machine learning for time series forecasting with Python (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFQ7g6wJTTDMYFrwWMxpbm |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Lazzeri, Francesca |t Machine Learning for Time Series Forecasting with Python |d Newark : John Wiley & Sons, Incorporated,c2020 |z 9781119682363 |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6420045 |z Texto completo |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6420045 | ||
| 994 | |a 92 |b IZTAP | ||