Based on the use-case type that we have in hand, the relationship between the number of temporal sequences and time can be distributed among multiple classes. Problems bucketed into each of these classes have different machine learning algorithms to handle them.
Classes of time series models
Stochastic time series model
Stochastic processes are random mathematical objects that can be defined using random variables. These data points are known to randomly change over time. Stochastic processes can again be divided into three main classes that are dependent on historic data points. They are autoregressive (AR) models, the moving average (MA) model, and integrated (I) models. These models combine to form the autoregressive...
