Systems and methods for distribution-transition estimation of key performance indicator (KPI). Exemplary embodiments include a method including establishing a state-space model in a computer memory, the model for handling measurement errors and modelling errors as noise, wherein time-varying indices have historical data and a correlation with KPI, adaptively estimating parameters related to the time varying indices, and obtaining residuals from the adaptive estimate of the parameters, estimating a probability distribution in order to generate random numbers from the probability distribution, generating random numbers related to the estimated probability distribution, thereby enabling prediction of future indicator distributions through a Sequential Monte Carlo method, providing models to input KPI intensity levels for a scenario level and to predict future values for a plurality of scenarios as point estimates and distributing and synthesizing the point estimation results for the plurality of scenarios based on KPI estimated probability distributions.