Book Image

Deep Learning for Time Series Cookbook

By : Vitor Cerqueira, Luís Roque
Book Image

Deep Learning for Time Series Cookbook

By: Vitor Cerqueira, Luís Roque

Overview of this book

Most organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise. This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You’ll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you’ll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions. By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.
Table of Contents (12 chapters)

Getting Started with Time Series

In this chapter, we introduce the main concepts and techniques used in time series analysis. The chapter begins by defining time series and explaining why the analysis of these datasets is a relevant topic in data science. After that, we describe how to load time series data using the pandas library. The chapter dives into the basic components of a time series, such as trend and seasonality. One key concept of time series analysis covered in this chapter is that of stationarity. We will explore several methods to assess stationarity using statistical tests.

The following recipes will be covered in this chapter:

  • Loading a time series using pandas
  • Visualizing a time series
  • Resampling a time series
  • Dealing with missing values
  • Decomposing a time series
  • Computing autocorrelation
  • Detecting stationarity
  • Dealing with heteroskedasticity
  • Loading and visualizing a multivariate time series
  • Resampling a multivariate time series
  • Analyzing the correlation among pairs of variables

By the end of this chapter, you will have a solid foundation in the main aspects of time series analysis. This includes loading and preprocessing time series data, identifying its basic components, decomposing time series, detecting stationarity, and expanding this understanding to a multivariate setting. This knowledge will serve as a building block for the subsequent chapters.