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.
| Main Author: | |
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| Format: | eBook |
| Language: | English |
| Published: |
Birmingham :
Packt Publishing, Limited,
2021.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of Contents:
- 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
- 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
- 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
- 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
- Amazon
- Implementations
- Change point detection
- Clustering
- Python practice
- 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