Machine Learning for Time-Series with Python
- Paperback: 371 pages
- Publisher: WOW! eBook (October 29, 2021)
- Language: English
- ISBN-10: 1801819629
- ISBN-13: 978-1801819626
Machine Learning for Time-Series with Python: Become proficient in deriving insights from time-series data and analyzing a model’s performance
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.
- 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
By the end of this Machine Learning for Time-Series with Python book, you will be proficient in effectively analyzing time-series datasets with machine learning principles.
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