Book Image

Julia Programming Projects

By : Adrian Salceanu
Book Image

Julia Programming Projects

By: Adrian Salceanu

Overview of this book

Julia is a new programming language that offers a unique combination of performance and productivity. Its powerful features, friendly syntax, and speed are attracting a growing number of adopters from Python, R, and Matlab, effectively raising the bar for modern general and scientific computing. After six years in the making, Julia has reached version 1.0. Now is the perfect time to learn it, due to its large-scale adoption across a wide range of domains, including fintech, biotech, education, and AI. Beginning with an introduction to the language, Julia Programming Projects goes on to illustrate how to analyze the Iris dataset using DataFrames. You will explore functions and the type system, methods, and multiple dispatch while building a web scraper and a web app. Next, you'll delve into machine learning, where you'll build a books recommender system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco business database. After metaprogramming, the final chapters will discuss dates and time, time series analysis, visualization, and forecasting. We'll close with package development, documenting, testing and benchmarking. By the end of the book, you will have gained the practical knowledge to build real-world applications in Julia.
Table of Contents (19 chapters)
Title Page
Copyright and Credits
About Packt

Understanding time series components

There are three components of time series that are key to understanding time-related data. They are trend, seasonality, and noise. Let's look at each of them in the context of our EU unemployment data.


The trend can be defined as the long-term tendency of the time series data—the fact that, on average, the values tend to increase or decrease over a period of time. Looking at our plot, we can identify three distinct trends:

A downward trend from 2005 until 2008 (less people unemployed on a year-on-year basis); an upward trend starting in 2008 and manifesting until 2013 (unemployment rose on average); and again, a downward trend between 2013, all the way until the end of 2017 (the number of people without work constantly decreased).


Seasonality is a regularly repeating pattern of highs and lows that is related to calendar time; that is, it's directly influenced by seasons, quarters, months, and so on. Think, for instance, about the electricity...