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211123 ||| eng |
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|a 1801816107
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| 050 |
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4 |
|a Q325.5
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| 100 |
1 |
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|a Auffarth, Ben
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| 245 |
0 |
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|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
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| 260 |
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|a Birmingham
|b Packt Publishing, Limited
|c 2021
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| 300 |
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|a 371 p.
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| 505 |
0 |
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|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
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| 505 |
0 |
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|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
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| 505 |
0 |
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|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
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| 505 |
0 |
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|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
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| 505 |
0 |
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|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
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| 653 |
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|a Time-series analysis / Computer programs / fast
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| 653 |
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|a Time-series analysis / Data processing / fast
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| 653 |
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a Python (Computer program language) / fast
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Time-series analysis / Data processing
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| 653 |
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|a Machine learning / fast
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| 653 |
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|a Apprentissage automatique
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| 653 |
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|a Time-series analysis / Computer programs / http://id.loc.gov/authorities/subjects/sh85135431
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| 653 |
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|a Python (Langage de programmation)
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| 653 |
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|a Série chronologique / Informatique
|
| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
|
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|b OREILLY
|a O'Reilly
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| 500 |
|
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|a Description based upon print version of record. - Adaptive learning methods
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| 015 |
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|a GBC1H4950
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| 776 |
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|z 9781801819626
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| 776 |
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|z 9781801816106
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| 776 |
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|z 1801819629
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| 856 |
4 |
0 |
|u https://learning.oreilly.com/library/view/~/9781801819626/?ar
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 006.31
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| 520 |
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|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
|