A stochastic process is a collection of random variables that represent a system evolving over time. These processes are used to model situations where outcomes are uncertain and can change unpredictably, such as stock prices or weather patterns. Each random variable in the process corresponds to a specific time point, capturing the inherent randomness of the system. Stochastic processes can be classified into different types, such as Markov processes, where the future state depends only on the current state, or Poisson processes, which model events occurring randomly over time. They are widely used in fields like finance, queueing theory, and physics to analyze and predict complex systems.