Abstract: According to an embodiment, a method for generating and explaining trend forecast of a timeseries with measures of quality of explainability is disclosed. The method comprises receiving a target variable and a set of relevant feature(s) corresponding to the variable. The method comprises performing a classification for the target variable, wherein the classification indicates classifying the target variable into a one or more states. Further, the method comprises determining a state transition matrices for each timestamp and design appropriate functions to model and quantify the trend forecast via a state transition score. The state transition score indicates transition between the corresponding states, wherein states may be obtained through suitable encoding of the target variable, and generating and explaining trend forecast based on the state transition.