| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240407b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781119682363 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.31 |
| Item number |
LAZ |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Lazzeri, Francesca |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Machine learning for time series forecasting with Python |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc |
Wiley, |
| Date of publication, distribution, etc |
2021 |
| Place of publication, distribution, etc |
USA : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xviii, 206 p. ; |
| Other physical details |
ill., |
| Dimensions |
23 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
59.95 |
| Price type code |
$ |
| Unit of pricing |
86.30 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Learn how to apply the principles of machine learning to time series modeling with this indispensable resource Machine Learning for Time Series Forecasting with Python 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. Despite the centrality of time series forecasting, few business analysts are familiar with the power or utility of applying machine learning to time series modeling. Author Francesca Lazzeri, a distinguished machine learning scientist and economist, corrects that deficiency by providing readers with comprehensive and approachable explanation and treatment of the application of machine learning to time series forecasting. Written for readers who have little to no experience in time series forecasting or machine learning, the book comprehensively covers all the topics necessary to: Understand time series forecasting concepts, such as stationarity, horizon, trend, and seasonality Prepare time series data for modeling Evaluate time series forecasting models' performance and accuracy Understand when to use neural networks instead of traditional time series models in time series forecasting Machine Learning for Time Series Forecasting with Python is full real-world examples, resources and concrete strategies to help readers explore and transform data and develop usable, practical time series forecasts. Perfect for entry-level data scientists, business analysts, developers, and researchers, this book is an invaluable and indispensable guide to the fundamental and advanced concepts of machine learning applied to time series modeling. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
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| Topical term or geographic name as entry element |
Python |
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| Topical term or geographic name as entry element |
Programming language |
|
|---|
| Topical term or geographic name as entry element |
Machine learning models |
|
|---|
| Topical term or geographic name as entry element |
Series forecasting |
|
|---|
| Topical term or geographic name as entry element |
Autoregressive |
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|---|
| Topical term or geographic name as entry element |
Data set |
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|---|
| Topical term or geographic name as entry element |
Demand forecasting |
|
|---|
| Topical term or geographic name as entry element |
Feature engineering |
|
|---|
| Topical term or geographic name as entry element |
Moving average |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |