Book Image

Mathematica Data Analysis

By : Sergiy Suchok
Book Image

Mathematica Data Analysis

By: Sergiy Suchok

Overview of this book

There are many algorithms for data analysis and it’s not always possible to quickly choose the best one for each case. Implementation of the algorithms takes a lot of time. With the help of Mathematica, you can quickly get a result from the use of a particular method, because this system contains almost all the known algorithms for data analysis. If you are not a programmer but you need to analyze data, this book will show you the capabilities of Mathematica when just few strings of intelligible code help to solve huge tasks from statistical issues to pattern recognition. If you're a programmer, with the help of this book, you will learn how to use the library of algorithms implemented in Mathematica in your programs, as well as how to write algorithm testing procedure. With each chapter, you'll be more immersed in the special world of Mathematica. Along with intuitive queries for data processing, we will highlight the nuances and features of this system, allowing you to build effective analysis systems. With the help of this book, you will learn how to optimize the computations by combining your libraries with the Mathematica kernel.
Table of Contents (10 chapters)
9
Index

Process models of time series


Since very often we observe random data, in order to predict it, we need to find the most suitable model that would describe the behavior of this data. A time series model that uses random variables is called a process. Thus, if a time series is a sequence which is strongly known to us (for example, as a result of observing), then the time series process is a random time series, and its values will be different every time depending on the values that the random magnitudes take.

There are several models of time series processes. In order to understand which model is most suitable for sampling data, it is necessary to explore each of them. Next, when we know that the time series refers to a specific type, we can compute the estimates for the model parameters and make a forecast on this basis. For this reason, let's review these models one after another and see how they are implemented in Mathematica.

The moving average model

The moving average model is specified...