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

Generating strong baseline forecasts

Time series forecasting has been around since the early 1920s, and through the years, many brilliant people have come up with different models, some statistical and some heuristic-based. I refer to them collectively as classical statistical models or econometrics models, although they are not strictly statistical/econometric.

In this section, we are going to review a few such models that can form really strong baselines when we want to try modern techniques in forecasting. As an exercise, we are going to use an excellent open source library for time series forecasting – darts (https://github.com/unit8co/darts). The 02-Baseline Forecasts using darts.ipynb notebook contains the code for this section so that you can follow along.

Before we start looking at forecasting techniques, let’s quickly understand how to use the darts library to generate the forecasts. We are going to pick one consumer from the dataset and try out all the...