Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting

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 series data, starting with ingesting series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized series databases such as InfluxDB. Next, you'll learn strats for handling missing data, dealing with 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 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 series data different from other data
Apply various imputation and interpolation strats for missing data
Implement different models for univariate and multivariate series
Use different deep learning libraries such as TensorFlow, Keras, and PyTorch
Plot interactive series visualizations using hvPlot
Explore state-space models and the unobserved components model (UCM)
Detect anomalies using statistical and machine learning methods
Forecast complex series with multiple seasonal patterns

Who this book is for

This book is for data analysts, business analysts, data scientists, data eeers, or Python developers who want practical Python recipes for series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with series data to solve business problems will also help you to better utilize and apply the different recipes in this book.



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