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.

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Spis treści

Time Series Analysis with Python Cookbook, 2E. Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation - Second Edition 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 & Diagnosis
• 10. Building Univariate Models using Statistical Methods
• 11. Advanced 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. Working with Multiple Seasonality in Time Series
• 16. Probabilistic Models for Time Series
• 17. Signal Processing Techniques for Time Series Analysis