Francesca Lazzeri - Machine Learning for Time Series Forecasting with Python

Visual Studio Code Python extension: Visual Studio Code Python extension is a Visual Studio Code extension with rich support for the Python language (for all actively supported versions of the language: 2.7, ≥ 3.5), including features such as IntelliSense, linting, debugging, code navigation, code formatting, Jupyter notebook support, refactoring, variable explorer, and test explorer.

Python 3: Python 3.0 was originally released in 2008 and is the latest major version of the language, with the latest version of the language, Python 3.8, being released in October 2019. In most of our examples in this book, we will use Python version 3.8.

It is important to note that Python 3.x is incompatible with the 2.x line of releases. The language is mostly the same, but many details, especially how built-in objects like dictionaries and strings work, have changed considerably, and a lot of deprecated features were finally removed. Here are some Python 3.0 resources:Python documentation ( python.org/doc/)Latest Python updates ( aka.ms/PythonMS)

If you have not already done so, install VS Code. Next, install the Python extension for VS Code from the Visual Studio Marketplace. For additional details on installing extensions, see Extension Marketplace. The Python extension is named Python and published by Microsoft.

Along with the Python extension, you need to install a Python interpreter, following the instructions below:

If you are using Windows:Install Python from python.org. You can typically use the Download Python button that appears first on the page to download the latest version.Note: If you don't have admin access, an additional option for installing Python on Windows is to use the Microsoft Store. The Microsoft Store provides installs of Python 3.7 and Python 3.8. Be aware that you might have compatibility issues with some packages using this method.For additional information about Python on Windows, see Using Python on Windows at python.org.

If you are using macOS:The system installation of Python on macOS is not supported. Instead, an installation through Homebrew is recommended. To install Python using Homebrew on macOS use brew install python3 at the Terminal prompt.Note: On macOS, make sure the location of your VS Code installation is included in your PATH environment variable. See these setup instructions for more information.

If you are using Linux:The built-in Python 3 installation on Linux works well, but to install other Python packages you must install pip with get-pip.py.

To verify that you have installed Python successfully on your machine, run one of the following commands (depending on your operating system):

Linux/macOS: Open a Terminal Window and type the following command:python3 --version

Windows: Open a command prompt and run the following command:py -3 --version

If the installation was successful, the output window should show the version of Python that you installed.

In this chapter, I walked you through the core concepts and steps to prepare your time series data for forecasting models. Through some practical examples of time series, we discussed some essential aspects of time series representations, modeling, and forecasting.

Specifically, we discussed the following topics:

Flavors of Machine Learning for Time Series Forecasting: In this section you learned a few standard definitions of important concepts, such as time series, time series analysis, and time series forecasting. You also discovered why time series forecasting is a fundamental cross-industry research area.

Supervised Learning for Time Series Forecasting: In this section you learned how to reshape your forecasting scenario as a supervised learning problem and, as a consequence, get access to a large portfolio of linear and nonlinear machine learning algorithms.

Python for Time Series Forecasting: In this section we looked at different Python libraries for time series data such as pandas, statsmodels, and scikit-learn.

Experimental Setup for Time Series Forecasting: This section provided you with a general guide for setting up your Python environment for time series forecasting.

In the next chapter, we will discuss some practical concepts such as the time series forecast framework and its applications. Moreover, you will learn about some of the caveats that data scientists working on forecasting projects may face. Finally, I will introduce a use case and some key techniques for building machine learning forecasting solutions successfully.

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