Introduction to TimeSeries with Python
This book is about machine learning for timeseries with Python, and you can see this chapter as a 101 class for timeseries. In this chapter, we'll introduce timeseries, the history of research into timeseries, and how to use Python for timeseries.
We'll start with what a timeseries is and its main properties. We'll then look at the history of the study of timeseries 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 timeseries and why Python is the goto language for doing machine learning with timeseries. Finally, I will describe how to install the most prominent libraries in Python for timeseries analysis and machine learning, and we'll cover the basics of Python as relevant to timeseries and machine learning.
We're going to cover the following topics:

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What Is a TimeSeries?
Since this is a book about timeseries data, we should start with a clarification of what we are talking about. In this section, we'll introduce timeseries 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 timeseries data. Large areas of micro and macroeconomics rely on applied statistics with an emphasis on timeseries analyses and modeling. The following are examples of timeseries 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...
TimeSeries and Forecasting – Past and Present
TimeSeries have been studied since antiquity, and since then, timeseries analysis and forecasting have come a long way. A variety of disciplines contributed to the development of techniques applied to timeseries, 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 timeseries from the time of the Industrial Revolution to modernity. We'll deal with the more technical and uptodate side of things in Chapter 4, Introduction to Machine Learning with TimeSeries.
There's still much...
Python for TimeSeries
For timeseries, 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.
Rank 
Language 
Ratings 
1 
C 
16.34% 
2 
Java 
11.29% 
3 
Python 
10.86% 
4 
C++ ... 
Summary
In this chapter, we've introduced timeseries, the history of research into timeseries, and Python for timeseries.
We started with a definition of timeseries and its main properties. We then looked at the history of the study of timeseries 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 timeseries and why Python is the goto language for doing machine learning with timeseries. Finally, I described how to install and use Python for timeseries analysis and machine learning, and we covered some of the basics of Python as relevant to timeseries and machine learning.
In the next chapter, we'll look at timeseries analysis with Python.