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Time Series Analysis with Python Cookbook
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Time Series Analysis with Python Cookbook
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Overview of this book
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 by 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.
Table of Contents (18 chapters)
Preface
Chapter 1: Getting Started with Time Series Analysis
Free Chapter
Chapter 2: Reading Time Series Data from Files
Chapter 3: Reading Time Series Data from Databases
Chapter 4: Persisting Time Series Data to Files
Chapter 5: Persisting Time Series Data to Databases
Chapter 6: Working with Date and Time in Python
Chapter 7: Handling Missing Data
Chapter 8: Outlier Detection Using Statistical Methods
Chapter 9: Exploratory Data Analysis and Diagnosis
Chapter 10: Building Univariate Time Series Models Using Statistical Methods
Chapter 11: Additional Statistical Modeling Techniques for Time Series
Chapter 12: Forecasting Using Supervised Machine Learning
Chapter 13: Deep Learning for Time Series Forecasting
Chapter 14: Outlier Detection Using Unsupervised Machine Learning
Chapter 15: Advanced Techniques for Complex Time Series
Index
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