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.

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Spis treści

Time Series Analysis with Python Cookbook. Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation eBook -- spis treści

• 1. Getting Started with Time Series Analysis
• 2. Reading Time Series Data from Files
• 3. Reading Time Series Data from Databases
• 4. Persisting Time Series Data to Files
• 5. Persisting Time Series Data to Databases
• 6. Working with Date and Time in Python
• 7. Handling Missing Data
• 8. Outlier Detection Using Statistical Methods
• 9. Exploratory Data Analysis and Diagnosis
• 10. Building Univariate Time Series Models Using Statistical Methods
• 11. Additional Statistical Modeling Techniques for Time Series
• 12. Forecasting Using Supervised Machine Learning
• 13. Deep Learning for Time Series Forecasting
• 14. Outlier Detection Using Unsupervised Machine Learning
• 15. Advanced Techniques for Complex Time Series