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

Chapter 9. Working with Dates, Times, and Time Series

We've had quite an amazing and rewarding journey through the realm of machine learning. We have learned how to use algorithms to classify labeled data and apply our findings to make recommendations. We have seen how to extract business value from raw, unlabeled information by using unsupervised machine learning and clustering algorithms. However, one key component has been missing from our analysis so far—the temporal dimension.

Time is money, the saying goes—and as such, organisations of all sizes, from small businesses to large corporations, to governments, to complex multinational institutions such as the European Union, continuously measure and monitor a multitude of economic indicators over time. To be meaningful, the data is collected at regular intervals, allowing analysts to identify hidden structures and patterns, and predict future developments based on past and present conditions. These values, measured regularly on a time scale...