Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation
Author: Tarek A. Atwan
Publisher finelybook 出版社: Packt Publishing (June 30, 2022)
Language 语言: English
Print Length 页数: 630 pages
ISBN-10: 1801075549
ISBN-13: 9781801075541
Book Description
Perform time series analysis and forecasting confidently with this Python code bank and reference manual
Key Features
Explore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithms
Learn different techniques for evaluating, diagnosing, and optimizing your models
Work with a variety of complex data with trends, multiple seasonal patterns, and irregularities
Time series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting.
This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you’ll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed Author: more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you’ll work with ML and DL models using TensorFlow and PyTorch.
Finally, you’ll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.
What you will learn
Understand what makes time series data different from other data
Apply various imputation and interpolation strategies for missing data
Implement different models for univariate and multivariate time series
Use different deep learning libraries such as TensorFlow, Keras, and PyTorch
Plot interactive time series visualizations using hvPlot
Explore state-space models and the unobserved components model (UCM)
Detect anomalies using statistical and machine learning methods
Forecast complex time series with multiple seasonal patterns
