#### Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Table of Contents (12 chapters)
Preface
Basic Packages, Functions, and Concepts
Free Chapter
Mathematical Plotting with Matplotlib
Working with Randomness and Probability
Geometric Problems
Finding Optimal Solutions
Miscellaneous Topics
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Time series, as the name suggests, tracks a value over a sequence of distinct time intervals. They are particularly important in the finance industry, where stock values are tracked over time and used to make predictions – known as forecasting – of the value at some future time. Good predictions coming from such data can be used to make better investments. Time series also appear in many other common situations, such as weather monitoring, medicine, and any places where data is derived from sensors over time.

Time series, unlike other types of data, do not usually have independent data points. This means that the methods that we use for modeling independent data will not be particularly effective. Thus, we need to use alternative techniques to model data with this property. There are two ways in which a value in a time series can depend on previous values. The first is where there is a direct relationship between the value...