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Time Series Analysis with Python Cookbook

Time Series Analysis with Python Cookbook

By : Tarek A. Atwan
4.8 (11)
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Time Series Analysis with Python Cookbook

Time Series Analysis with Python Cookbook

4.8 (11)
By: Tarek A. Atwan

Overview of this book

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.
Table of Contents (18 chapters)
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Writing time series data to a relational database (PostgreSQL and MySQL)

In this recipe, you will write your DataFrame to a relational database (PostgreSQL). The approach is the same for any relational database system that is supported by the SQLAlchemy Python library. You will experience how SQLAlchemy makes it simple to switch the backend database (called dialect) without the need to alter the code. The abstraction layer provided by the SQLAlchemy library makes it feasible to switch to any supported database, such as from PostgreSQL to MySQL, using the same code.

The sample list of supported relational databases (dialects) in SQLAlchemy includes the following:

  • Microsoft SQL Server
  • MySQL/MariaDB
  • PostgreSQL
  • Oracle
  • SQLite

Additionally, there are external dialects available to install and use with SQLAlchemy to support other databases (dialects), such as Snowflake, AWS RedShift, and Google BigQuery. Please visit the official page of SQLAlchemy for a...

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