To use time-series data and develop a model, you need to understand the patterns in the data over time. These patterns are classified into four components, which are:
- Trend
It represents the gradual change in the time series data. The trend pattern depicts long-term growth or decline.
- Level
It refers to the baseline values for the series data if it were a straight line.
- Seasonality
It represents the short-term patterns that occur within a single unit of time and repeats indefinitely.
- Noise
It represents irregular variations and is purely random. These fluctuations are unforeseen, unpredictable, and cannot be explained by the model.