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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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Forecasting with an RNN using PyTorch

In the previous recipes, you used Keras to build different deep learning architectures with minimal changes to code. This is one of the advantages of a high-level API – it allows you to explore and experiment with different architectures very easily.

In this recipe, you will build a simple RNN architecture using PyTorch, a low-level API.

Getting ready

You will be using the functions and steps used to prepare the time series for supervised learning. The one exception is with the features_target_ts function, it will be modified to return a PyTorch Tensor object as opposed to a NumPy ndarray object. In PyTorch, tensor is a data structure similar to NumPy's ndarray object but optimized to work with Graphical Processing Units (GPUs).

You can convert a NumPy ndarray to a PyTorch Tensor object using the torch.from_numpy() method and convert a PyTorch Tensor object to a NumPy ndarray object using the detach.numpy() method:

numpy_array...

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