Book Image

Modern Time Series Forecasting with Python

By : Manu Joseph
5 (1)
Book Image

Modern Time Series Forecasting with Python

5 (1)
By: Manu Joseph

Overview of this book

We live in a serendipitous era where the explosion in the quantum of data collected and a renewed interest in data-driven techniques such as machine learning (ML), has changed the landscape of analytics, and with it, time series forecasting. This book, filled with industry-tested tips and tricks, takes you beyond commonly used classical statistical methods such as ARIMA and introduces to you the latest techniques from the world of ML. This is a comprehensive guide to analyzing, visualizing, and creating state-of-the-art forecasting systems, complete with common topics such as ML and deep learning (DL) as well as rarely touched-upon topics such as global forecasting models, cross-validation strategies, and forecast metrics. You’ll begin by exploring the basics of data handling, data visualization, and classical statistical methods before moving on to ML and DL models for time series forecasting. This book takes you on a hands-on journey in which you’ll develop state-of-the-art ML (linear regression to gradient-boosted trees) and DL (feed-forward neural networks, LSTMs, and transformers) models on a real-world dataset along with exploring practical topics such as interpretability. By the end of this book, you’ll be able to build world-class time series forecasting systems and tackle problems in the real world.
Table of Contents (26 chapters)
1
Part 1 – Getting Familiar with Time Series
6
Part 2 – Machine Learning for Time Series
13
Part 3 – Deep Learning for Time Series
20
Part 4 – Mechanics of Forecasting

Single-step-ahead recurrent neural networks

Although we took a little detour to check out how DL regression models can be used to train the same global models we learned about in Chapter 10, Global Forecasting Models, now we are back to looking at DL models and architectures specifically built for time series. And as always, we will look at simple one-step-ahead and local models first before moving on to more complex modeling paradigms. In fact, we have another chapter (Chapter 15, Strategies for Global Deep Learning Forecasting Models) entirely devoted to techniques we can use to train global DL models.

Now, let’s bring our attention back to one-step-ahead local models. We saw recurrent neural networks (RNNs) (vanilla RNN, long short-term memory (LSTM), and gated recurrent unit (GRU)) as a few blocks we can use for sequences such as time series. Now, let’s see how we can use them in an end-to-end (E2E) model on the dataset we have been working on (the London smart...