Patterns in time series data are the backbone of the analysis of it. As with other fields of statistics and, in particular, the field of machine learning, one of the primary goals of time series analysis is to identify patterns in data. Those patterns can then be utilized to provide meaningful insights about both past and future events such as seasonal, outliers, or unique events. Patterns in time series analysis can be categorized into one of the following:
- Structural patterns: These are also known as series components, which represent, as the name implies, the core structure of the series. There are three types of structural patterns—trend, cycle, and seasonal. You can think about those patterns as binary events, which may or may not exist in the data. This helps to classify the series characteristics and identify the best approach to analyze...
