Machine Learning for Time-Series with Python: Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
- Length: 371 pages
- Edition: 1
- Language: English
- Publisher: Packt Publishing
- Publication Date: 2021-11-09
- ISBN-10: 1801819629
- ISBN-13: 9781801819626
- Sales Rank: #0 (See Top 100 Books)
Become proficient in deriving insights from time-series data and analyzing a model’s performance
Key Features
- Explore popular and modern machine learning methods including the latest online and deep learning algorithms
- Learn to increase the accuracy of your predictions by matching the right model with the right problem
- Master time-series via real-world case studies on operations management, digital marketing, finance, and healthcare
Book Description
Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.
This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with loading time-series datasets from any source, deep learning models like recurrent neural networks and causal convolutional network models, and gradient boosting with feature engineering.
Machine Learning for Time-Series with Python explains the theory behind several useful models and guides you in matching the right model to the right problem. The book also includes real-world case studies covering weather, traffic, biking, and stock market data.
By the end of this book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.
What you will learn
- Understand the main classes of time-series and learn how to detect outliers and patterns
- Choose the right method to solve time-series problems
- Characterize seasonal and correlation patterns through autocorrelation and statistical techniques
- Get to grips with time-series data visualization
- Understand classical time-series models like ARMA and ARIMA
- Implement deep learning models like Gaussian processes and transformers and state-of-the-art machine learning models
- Become familiar with many libraries like prophet, xgboost, and TensorFlow
Who This Book Is For
This book is ideal for data analysts, data scientists, and Python developers who are looking to perform time-series analysis to effectively predict outcomes. Basic knowledge of the Python language is essential. Familiarity with statistics is desirable.
Table of Contents
- Introduction to Time Series with Python
- Time-Series Analysis with Python
- Preprocessing Time-Series
- Machine Learning for Time-Series
- Time-Series Forecasting with Moving Averages and Autoregressive Models
- Unsupervised Methods for Time Series
- Machine Learning Models for Time Series
- Online Learning for Time Series
- Probabilistic Models
- Deep Learning for Time Series
- Reinforcement Learning for Time-Series
- Case Studies
Preface Who this book is for What this book covers To get the most out of this book Get in touch 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 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 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 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 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 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 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 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 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 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 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 Multivariate Forecasting Forecasting a Multivariate Time-Series Python practice What's next for time-series? Other Books You May Enjoy Index
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