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 stationarity

A time series is considered stationary if its statistical properties such as mean, variance, autocorrelation, and so on, are constant over time. Stationarity is important because most forecasting models run on the assumption that the time series is stationary or can be rendered (approximately) stationary using transformations. The reason for this approach is that values in a stationary time series are much easier to predict—if its properties are constant, we can simply state that they will be in the future as they were in the past. Once we forecast future values based on stationary time series, we can then reverse the process and the transformations to compute the values that would match the original series.

Thus, the properties of a stationary time series do not depend on the time when the series is observed. Implicitly, this means that time series that present seasonality or trends are not stationary. In this context, again, we must be careful of the difference...