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

Purchase of the print or Kindle book includes a free PDF eBook
Key FeaturesExplore up-to-date forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithmsLearn different techniques for evaluating, diagnosing, and optimizing your modelsWork with a variety of complex data with trends, multiple seasonal patterns, and irregularitiesBook 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 Descriptions, 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 learnUnderstand what makes time series data different from other dataApply imputation and interpolation strategies to handle missing dataImplement an array of models for univariate and multivariate time seriesDescription interactive time series visualizations using hvDescriptionExplore state-space models and the unobserved components model (UCM)Detect anomalies using statistical and machine learning methodsForecast complex time series with multiple seasonal patternsUse conformal prediction for constructing prediction intervals for time seriesWho this book is for
This book is for data analysts, business analysts, data scientists, data engineers, and Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is a prerequisite. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.
Table of ContentsGetting Started with Time Series AnalysisReading Time Series Data from FilesReading Time Series Data from DatabasesPersisting Time Series Data to FilesPersisting Time Series Data to DatabasesWorking with Date and Time in PythonHandling Missing DataOutlier Detection Using Statistical MethodsExploratory Data Analysis & DiagnosisBuilding Univariate Models using Statistical MethodsAdvanced Statistical Modeling Techniques for Time SeriesForecasting Using Supervised Machine LearningDeep Learning for Time Series ForecastingOutlier Detection Using Unsupervised Machine LearningWorking with Multiple Seasonality in Time Series(N.B. Please use the Read Sample option to see further chapters)

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