#Time sequence analysis series

Even though it is very common to use time as an axis in datasets, not all collected data is time series.

#Time sequence analysis series

Now let’s clarify what kinds of data are not time series to get this out of the way. So having time as one of the main axes would be the main indicator that a given dataset is a time series. The time intervals applied to assemble the collected data in a chronological order are called the time series frequency. Time is the central attribute that distinguishes time series from other types of data. Or, to put it simply, time series is data (observations or behavior) collected at different points in time and organized chronologically. It can be defined as a collection of observations for a single subject assembled over different, generally equally spaced, time intervals.

Basically, time series data is any type of information presented as an ordered sequence. Let’s begin by clearly defining what it actually is, as well as what it isn’t. We will learn the main concepts related to gathering, organizing and usage, including types and formats, ways to store and collect it, as well as diving into the fundamentals of analysis and visualization techniques. In this article we will summarize all the knowledge that you may need in order to use time series data as a way to gain business insights or conduct a study. Sensors, monitoring, weather forecasts, stock prices, exchange rates, application performance metrics are just a few examples. Time series data is omnipresent in our lives as we can encounter it in pretty much any domain.