Time Series Analysis With Python Cookbook Second Edition Early Access 2nd Tarek A Atwan by Tarek A. Atwan 9781805124283, 1805124285 instant download after payment.

Key Features

Explore up-to-date 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

Book Description

To use time series data to your advantage, you need to be well-versed in data preparation, analysis, and forecasting. This fully updated second edition includes chapters on probabilistic models and signal processing techniques, as well as new content on transformers. Additionally, you will leverage popular libraries and their latest releases covering Pandas, Polars, Sktime, stats models, stats forecast, Darts, and Prophet for time series with new and relevant examples.

You'll start by ingesting time series data from various sources and formats, and learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods.

Further, you'll explore forecasting using classical statistical models (Holt-Winters, SARIMA, and VAR). Learn practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Then we will move into more advanced topics such as building ML and DL models using TensorFlow and PyTorch, and explore probabilistic modeling techniques. In this part, you’ll also learn how to evaluate, compare, and optimize models, making sure that you finish this book well-versed in wrangling data with Python.

What you will learn

Understand what makes time series data different from other data

Apply imputation and interpolation strategies to handle missing data

Implement an array of models for