Machine learning for time-series with Python : forecast, predict, and detect anomalies with state-of-the-art machine learning methods

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.

Bibliographic Details
Other Authors: Auffarth, Ben, author (author)
Format: eBook
Language:Inglés
Published: Birmingham : Packt Publishing, Limited [2021]
Subjects:
Machine learning.
Time-series analysis > Data processing.
Time-series analysis > Computer programs.
Python (Computer program language)
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009644271706719
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
  • Google
  • Amazon
  • Facebook
  • Twitter
  • 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
  • Adaptive learning methods
  • Python practice
  • Drift detection
  • Regression
  • Model selection
  • Summary
  • Chapter 9: Probabilistic Models for Time-Series
  • Probabilistic Models for Time-Series
  • Prophet
  • Markov Models
  • Fuzzy Modeling
  • Bayesian Structural Time-Series Models
  • Python Exercise
  • Prophet
  • Markov Switching Model
  • Fuzzy Time-Series
  • Bayesian Structural Time-Series Modeling
  • Summary
  • Chapter 10: Deep Learning for Time-Series
  • Introduction to deep learning
  • Deep learning for time series
  • Autoencoders
  • InceptionTime
  • DeepAR
  • N-BEATS
  • Recurrent neural networks
  • ConvNets
  • Transformer architectures
  • Informer
  • Python practice
  • Fully connected network
  • Recurrent neural network.
  • Dilated causal convolutional neural network
  • Summary
  • Chapter 11: Reinforcement Learning for Time-Series
  • Introduction to reinforcement learning
  • Reinforcement Learning for Time-Series
  • Bandit algorithms
  • Deep Q-Learning
  • Python Practice
  • Recommendations
  • Trading with DQN
  • Summary
  • Chapter 12: Multivariate Forecasting
  • Forecasting a Multivariate Time-Series
  • Python practice
  • What's next for time-series?
  • Why subscribe?
  • Packt Page
  • Other Books You May Enjoy
  • Index.

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