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
Dedication
About Packt
Contributors
Preface
Index

Chapter 10. Time Series Forecasting

In the previous chapter, we learned how to handle date and time with Julia. This allowed us to understand the very important concept of time series data. Now, we are ready to discuss yet another highly important data science topic—time series analysis.

Time series analysis and forecasting represents a key strategic and decisive component of any organization, from understanding top sales periods to end of season intervals and discounts, scheduling employees' time off, budgets, fiscal years, product release cycles, increased demand in raw materials, and many, many other aspects. Understanding and predicting the evolution of various business indicators over time is a necessary part of doing business, whether we're talking about a school, a billion dollar corporation, a hotel, a supermarket, or a government.

However, time series data analysis is one of the most fairly complex tasks of data science. The nature and particularities of chronological events led to...