# Components of Time Series based Data

## What is Time Series Analysis and How is it Used?

Time series is a sequence of data points in chronological sequence, most often gathered in regular intervals. Time series analysis can be applied to any variable that changes over time and generally speaking, usually, data points that are closer together are more similar than those further apart.

### Time Series Data Components

Most often, the components of time series data will include a trend, seasonality, noise or randomness, a curve, and the level. Before we go on to defining these terms, it’s important to note that not all time series data will include every one of these time series components. For instance, audio files that are taken in sequence are examples of time series data, however, they won’t contain a seasonal component (although note they would have periodic cycles). On the other hand, most business data will likely contain seasonality such as retail sales peaking in the fourth quarter.

Here are the various components that can occur in time series data:

1. Level: When you read about the “level” or the “level index” of time series data, it’s referring to the mean of the series.

2. Noise: All-time series data will have noise or randomness in the data points that aren’t correlated with any explained trends. Noise is unsystematic and is short-term.

3. Seasonality: If there are regular and predictable fluctuations in the series that are correlated with the calendar – could be quarterly, weekly, or even days of the week, then the series includes a seasonality component. It’s important to note that seasonality is domain-specific, for example, real estate sales are usually higher in the summer months versus the winter months while regular retail usually peaks during the end of the year. Also, not all-time series have a seasonal component, as mentioned for audio or video data.

4. Trend: When referring to the “trend” in time series data, it means that the data has a long-term trajectory which can either be trending in the positive or negative direction. An example of a trend would be a long-term increase in a company’s sales data or network usage.

5. Cycle: Repeating periods that are not related to the calendar. This includes business cycles such as economic downturns or expansions or salmon run cycles, or even audio files which have cycles but aren’t related to the calendar in the weekly, monthly, or yearly sense.