Time Series Patterns⌛

What is Time Series?

A dataset consisting of observation values arranged over time. Examples include stock market data, weather data, etc.

The concepts of Stationary, Trend, Seasonality,and Cycle are crucial for interpreting time series data…

Stationary

Stationary refers to the statistical properties of a series remaining constant over time. If the mean, variance and covariance of a time series remain constant throughout time, the series is considered stationary. Theoretically, when time series data follows a specific pattern, it becomes more predictable. If a time series is stationary, predictions can be made more comfortably. In cases where non-stationarity is observed the difference of the series is taken. For instance, if there are values on Mondays and also on Sundays at time T, these two values are subtracted from each other, making the series stationary.

Trend

It is one of the most critical topics for the time series domain. Awareness of the concept of Trend is essential in every field. The structure indicating the long-term increase or decrease in a time series is referred to as a trend. If there is a trend, the probability of the series being stationary is very low.