Time series often display periodic behavior so that peaks or dips in the value appear at regular intervals. This behavior is called seasonality in the analysis of time series. The methods we have used to far in this chapter to model time series data obviously do not account for seasonality. Fortunately, it is relatively easy to adapt the standard ARIMA model to incorporate seasonality, resulting in what is sometimes called a SARIMA model.
In this recipe, we will learn how to model time series data that includes seasonal behavior and use this model to produce forecasts.
Getting ready
For this recipe, we will need the NumPy package imported as np, the Pandas package imported as pd, the Matplotlib pyplotmodule as plt, and the statsmodels apimodule imported as sm. We will also need the utility for creating sample time series data from the tsdatamodule, which is included in this book's repository:
from tsdata...
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Book Overview & Buying
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Table Of Contents
Applying Math with Python
By :
Applying Math with Python
By:
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 (13 chapters)
Preface
Chapter 1: An Introduction to Basic Packages, Functions, and Concepts
Free Chapter
Chapter 2: Mathematical Plotting with Matplotlib
Chapter 3: Calculus and Differential Equations
Chapter 4: Working with Randomness and Probability
Chapter 5: Working with Trees and Networks
Chapter 6: Working with Data and Statistics
Chapter 7: Using Regression and Forecasting
Chapter 8: Geometric Problems
Chapter 9: Finding Optimal Solutions
Chapter 10: Improving Your Productivity
Index
