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Machine Learning for Time-Series with Python Forecast, Predict, and Detect Anomalies with State-Of-the-art Machine Learning Methods.
The book contains the most common as well as state-of-the-art methods in machine learning for time-series, and examples that every data scientist or analyst would have encountered, if not in their job, then in a job interview.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2021.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 082 | 0 | 4 | |a 006.31 |2 23 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Auffarth, Ben. | |
| 245 | 1 | 0 | |a Machine Learning for Time-Series with Python |h [electronic resource] : |b Forecast, Predict, and Detect Anomalies with State-Of-the-art Machine Learning Methods. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2021. | ||
| 300 | |a 1 online resource (371 p.) | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a computer |2 rdamedia | ||
| 338 | |a online resource |2 rdacarrier | ||
| 347 | |a text file | ||
| 500 | |a Description based upon print version of record. | ||
| 505 | 0 | |a Cover -- Copyright -- Contributors -- Table of Contents -- Preface -- Chapter 1: Introduction to Time Series with Python -- What Is a Time Series? -- Characteristics of Time Series -- Time Series and Forecasting -- Past and Present -- Demography -- Genetics -- Astronomy -- Economics -- Meteorology -- Medicine -- Applied Statistics -- Python for Time Series -- Installing libraries -- Jupyter Notebook and JupyterLab -- NumPy -- pandas -- Best practice in Python -- Summary -- Chapter 2: Time-Series Analysis with Python -- What is time series analysis? -- Working with time series in Python | |
| 505 | 8 | |a Requirements -- Datetime -- pandas -- Understanding the variables -- Uncovering relationships between variables -- Identifying trend and seasonality -- Summary -- Chapter 3: Preprocessing Time Series -- What Is Preprocessing? -- Feature Transforms -- Scaling -- Log and Power Transformations -- Imputation -- Feature Engineering -- Date- and Time-Related Features -- ROCKET -- Shapelets -- Python Practice -- Log and Power Transformations in Practice -- Imputation -- Holiday Features -- Date Annotation -- Paydays -- Seasons -- The Sun and Moon -- Business Days -- Automated Feature Extraction | |
| 505 | 8 | |a ROCKET -- Shapelets in Practice -- Summary -- Chapter 4: Introduction to Machine Learning for Time-Series -- Machine learning with time series -- Supervised, unsupervised, and reinforcement learning -- History of machine learning -- Machine learning workflow -- Cross-validation -- Error metrics for time series -- Regression -- Classification -- Comparing time-series -- Machine learning algorithms for time-series -- Distance-based approaches -- Shapelets -- ROCKET -- Time Series Forest and Canonical Interval Forest -- Symbolic approaches -- HIVE-COTE -- Discussion -- Implementations -- Summary | |
| 505 | 8 | |a Chapter 5: Time-Series Forecasting with Moving Averages and Autoregressive Models -- What are classical models? -- Moving average and autoregression -- Model selection and order -- Exponential smoothing -- ARCH and GARCH -- Vector autoregression -- Python libraries -- Statsmodels -- Python practice -- Requirements -- Modeling in Python -- Summary -- Chapter 6: Unsupervised Methods for Time-Series -- Unsupervised methods for time-series -- Anomaly detection -- Microsoft -- Google -- Amazon -- Facebook -- Twitter -- Implementations -- Change point detection -- Clustering -- Python practice | |
| 505 | 8 | |a Requirements -- Anomaly detection -- Change point detection -- Summary -- Chapter 7: Machine Learning Models for Time-Series -- More machine learning methods for time series -- Validation -- K-nearest neighbors with dynamic time warping -- Silverkite -- Gradient boosting -- Python exercise -- Virtual environments -- K-nearest neighbors with dynamic time warping in Python -- Silverkite -- Gradient boosting -- Ensembles with Kats -- Summary -- Chapter 8: Online Learning for Time-Series -- Online learning for time series -- Online algorithms -- Drift -- Drift detection methods | |
| 500 | |a Adaptive learning methods. | ||
| 520 | |a The book contains the most common as well as state-of-the-art methods in machine learning for time-series, and examples that every data scientist or analyst would have encountered, if not in their job, then in a job interview. | ||
| 542 | |f Copyright © 2021 Packt Publishing |g 2021 | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Machine learning. | |
| 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 Apprentissage automatique. | |
| 650 | 6 | |a Série chronologique |x Informatique. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 7 | |a Machine learning |2 fast | |
| 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 | |
| 776 | 0 | 8 | |i Print version: |a Auffarth, Ben |t Machine Learning for Time-Series with Python |d Birmingham : Packt Publishing, Limited,c2021 |z 9781801819626 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781801819626/?ar |z Texto completo (Requiere registro previo con correo institucional) |
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| 938 | |a ProQuest Ebook Central |b EBLB |n EBL6797223 | ||
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