To get the most out of this book

• You should have a basic knowledge of Python to get started.
• All notebooks used in this book come with links to Google Colab, where you should be able to execute them.

Download the example code files

The code bundle for the book is hosted on GitHub at https://github.com/PacktPublishing/Machine-Learning-for-Time-Series-with-Python. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781801819626_ColorImages.pdf.

Conventions used

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. For example; "Let's use UCBRegressor to select the best learning rate for a linear regression model."

A block of code is set as follows:

import numpy as np
import pandas as pd
from keras.layers import Conv1D, Input, Add, Activation, Dropout
from keras.models import Sequential, Mode

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

owid_covid["date"] = pd.to_datetime(owid_covid["date"]

Any command-line input or output is written as follows:

pip install xgboost

Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes, also appear in the text like this. For example: "The task of identifying, quantifying, and decomposing these and other characteristics is called time-series analysis."