You're reading from Machine Learning for Time-Series with Python

Product typeBook

Published inOct 2021

PublisherPackt

ISBN-139781801819626

Edition1st Edition

Concepts

Machine Learning

Author (1)

Ben Auffarth

Introduction to Time-Series with Python

This book is about machine learning for time-series with Python, and you can see this chapter as a 101 class for time-series. In this chapter, we'll introduce time-series, the history of research into time-series, and how to use Python for time-series.

We'll start with what a time-series is and its main properties. We'll then look at the history of the study of time-series in different scientific disciplines foundational to the field, such as demography, astronomy, medicine, and economics.

Then, we'll go over the capabilities of Python for time-series and why Python is the go-to language for doing machine learning with time-series. Finally, I will describe how to install the most prominent libraries in Python for time-series analysis and machine learning, and we'll cover the basics of Python as relevant to time-series and machine learning.

We're going to cover the following topics:

...

What Is a Time-Series?

Since this is a book about time-series data, we should start with a clarification of what we are talking about. In this section, we'll introduce time-series and their characteristics, and we'll go through different kinds of problems and types of analyses relevant to machine learning and statistics.

Many disciplines, such as finance, public administration, energy, retail, and healthcare, are dominated by time-series data. Large areas of micro- and macro-economics rely on applied statistics with an emphasis on time-series analyses and modeling. The following are examples of time-series data:

Daily closing values of a stock index
Number of weekly infections of a disease
Weekly series of train accidents
Rainfall per day
Sensor data such as temperature measurements per hour
Population growth per year
Quarterly earnings of a company over a number of years

This is only to name but a few. Any data...

Time-Series and Forecasting – Past and Present

Time-Series have been studied since antiquity, and since then, time-series analysis and forecasting have come a long way. A variety of disciplines contributed to the development of techniques applied to time-series, including mathematics, astronomy, demographics, and statistics. Many innovations came initially from mathematics, later statistics, and finally machine learning. Many innovations in applied statistics had their origins in demography (used in public administration), economics, or other fields.

In this section, I'll sketch the development path from simpler methods leading up to the machine learning methods available today. I'll try to chart the development of concepts relevant to time-series from the time of the Industrial Revolution to modernity. We'll deal with the more technical and up-to-date side of things in Chapter 4, Introduction to Machine Learning with Time-Series.

There's still much...

Python for Time-Series

For time-series, there are two main languages, R and Python, and it's worth briefly comparing the two and describing what makes Python special. Python is one of the top programming languages by popularity. According to the TIOBE from February 2021, it is only surpassed in popularity by C and Java.

Summary

In this chapter, we've introduced time-series, the history of research into time-series, and Python for time-series.

We started with a definition of time-series and its main properties. We then looked at the history of the study of time-series in different scientific disciplines, such as demography and genetics, astronomy, economics, meteorology, medicine, and applied statistics.

Then, we went over the capabilities of Python for time-series and why Python is the go-to language for doing machine learning with time-series. Finally, I described how to install and use Python for time-series analysis and machine learning, and we covered some of the basics of Python as relevant to time-series and machine learning.

In the next chapter, we'll look at time-series analysis with Python.

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Machine Learning for Time-Series with Python

Published in: Oct 2021Publisher: PacktISBN-13: 9781801819626

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Author (1)

Ben Auffarth

Ben Auffarth is a full-stack data scientist with more than 15 years of work experience. With a background and Ph.D. in computational and cognitive neuroscience, he has designed and conducted wet lab experiments on cell cultures, analyzed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores, built production systems processing hundreds and thousands of transactions per day, and trained language models on a large corpus of text documents. He co-founded and is the former president of Data Science Speakers, London.
Read more about Ben Auffarth

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Rank	Language	Ratings
1	C	16.34%
2	Java	11.29%
3	Python	10.86%
4	C++ ...