**Time Series Data**What do the stockbrokers and airplane companies have in common? The answer to this riddle is: They both use time-series data. In this article, we are going to explain the concept of time-series data. We will discover how it differs from panel or cross-sectional data and

**why**time-series analysis is tricky. After that, we’ll introduce you to some of the most basic time-series notation and terminology. All of this should give you a good idea of the role time series play in data science! Let’s begin with the definition.

## What is a Time Series?

A time series is a sequence of information that attaches a time period to each value. The value can be pretty much anything measurable that depends on time in some way, like prices, humidity, or a number of people. As long as the values we record are unambiguous, any medium could be measured with time series.## What Are Some Prominent Features of Time Series?

### Time Period

For starters, there aren’t any limitations regarding the total time span of a time series. It could be a minute, a day, a month, or even a century. All that’s needed is a starting and an ending point. Of course, there are usually numerous points in-between and the interval of time separating two consecutive ones is called a “time period”. For example, if the data was recorded once per day from 1/1/2000 to New Year’s Eve 2009, a single time period would be a day, while the entire time span would be a decade.### Frequency

The “frequency” of the dataset tells us how often the values of the data set are recorded. To be able to analyse time series in a meaningful way, all time-periods must be**equal and clearly defined**. This, in turn, results in a

**constant**frequency, so you see how the two features are related. This frequency is a measurement of time and could range from a few milliseconds to several decades. However, the ones we most commonly encounter are daily, monthly, quarterly and annual.

### Patterns

Lastly, we can expect the patterns we observe in time-series to persist in the future. That is why we often try to predict the future by analysing recorded values. Now that you’re familiar with the main features of time-series data, let’s look at some examples.## How Do We Use Time Series Data in Weather Prediction?

Meteorologists often cope with the task of**forecasting**the weather for days ahead. To make even remotely accurate predictions on a consistent basis, they rely on analysing past data. That said, if the data is not ordered chronologically, finding the correct pattern would be extremely difficult. For instance, simply knowing the highest temperature for the last 5 days would be useless unless we know which value corresponds to each day. Why? Because chances that the temperature rose 5 days in a row or dropped 5 days in a row are equal. Thus, without the corresponding time periods for each value, the data is much less relevant.