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

Decomposing a time series

Seasonal decomposition is the process by which we deconstruct a time series into its components – typically, trend, seasonality, and residuals. The general approach for decomposing a time series is as follows:

  1. Detrending: Here, we estimate the trend component (which is the smooth change in the time series) and remove it from the time series, giving us a detrended time series.
  2. Deseasonalizing: Here, we estimate the seasonality component from the detrended time series. After removing the seasonal component, what is left is the residual.

Let’s discuss them in detail.

Detrending

Detrending can be done in a few different ways. Two popular ways of doing it are by using moving averages and locally estimated scatterplot smoothing (LOESS) regression.

Moving averages

One of the easiest ways of estimating trends is by using a moving average along the time series. It can be seen as a window that is moved along the time series...