A specific class of problems within the domain of data analysis and often associated with supervised supervised learning tasks such as classification and regression is collectively known as time series analysis and its distinguishing feature is that it is concerned with temporal data.
By time series we mean a series of data, where each item is associated with some temporal information, e.g. the value of a certain commodity over time, the number of positive Covid-19 tests by day etc.
Time series analysis involves several distinct sub-problems such as time series decomposition (i.e. decomposing a time series into its various components such as a trend, a seasonal component, etc.) or forecasting (predicting future values).
Seasonal decomposition on the (normalized) number of searches for the keyword “vacation”.
The way a time series needs to be handled and the kinds of methods that are likely to be useful generally depend on its characteristics: e.g. whether a time series is stationary (its overall character does not change over time), whether it is (fully or at least partially) auto-regressive (future values can be predicted from past values) and so on.