Bayesian ARIMA

Bayesian ARIMA is a statistical method used to forecast future data points by analyzing past trends, incorporating uncertainty in the predictions. It combines the ARIMA model, which captures patterns like trends and seasonal effects in time series data, with Bayesian inference, a way to update beliefs about the model's parameters based on new information. This approach provides a probabilistic framework, offering not just a single forecast but a range of possible outcomes with associated confidence levels. Essentially, Bayesian ARIMA allows for more flexible and informed predictions by systematically accounting for uncertainty and prior knowledge in the modeling process.