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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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Chapter 15: Advanced Techniques for Complex Time Series

Time series data can contain complex seasonality – for example, recorded hourly data can exhibit daily, weekly, and yearly seasonal patterns. With the rise of connected devices – for example, the Internet of Things (IoT) and sensors – data is being recorded more frequently. For example, if you examine classical time series datasets used in many research papers, many were smaller sets and recorded less frequently, such as annually or monthly. Such data contains one seasonal pattern. More recent datasets and research now use higher frequency data, recorded in hours or minutes.

Many of the algorithms we used in earlier chapters can work with seasonal time series. Still, they assume there is only one seasonal pattern, and their accuracy and results will suffer on more complex datasets.

In this chapter, you will explore new algorithms that can model a time series with multiple seasonality for forecasting...

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