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

Time series forecasting

Forecasting implies identifying models that fit the historical data and using them to predict future values. When forecasting time series data, decomposition plays a very important part, helping to make predictions more accurate. The underlying assumption is that we can be more precise if we forecast each component individually, using the best-suited method, and then sum or multiply the parts (depending on whether the model is additive or multiplicative) to compute the final value.


This is the simplest method, stating that the forecasted value is equal to the last value in the series. As mentioned previously, this is used with random walk models, where future movements are unpredictable. For example, to predict the value for the first unknown month, January 2018, using the naïve model, we can take the seasonally adjusted value from December 2017 and add (multiply) the seasonal component of the month of January:

julia> update(unemployment_data, Date(2018, 1,...